Less Interesting, Less Enjoyable: Why AI-Assisted Classes Feel Less Important
'Cognitive Surrender': Faster Solutions, Lower Test Scores Show How AI is Eroding Math Skills
Is Everyone Using AI? How False Perceptions Can Become Self-fulfilling
Overworked and Understaffed: Special Ed Teachers Turn to AI for Help
Should Schools Get Rid of Homework? The Answer is Complex and AI Contributes
Feedback Bias? How AI Adjusts Replies Based on Race and Gender, Research Finds
Do You Like AI Because AI Likes You? How AI Flattery Crosses Signals
The Quest to Build a Better AI Tutor
The AI ‘Hivemind’: Why So Many Student Essays Sound Alike
Major support for MindShift comes from
Player sponsored by
window.__IS_SSR__=true
window.__INITIAL_STATE__={
"attachmentsReducer": {
"audio_0": {
"type": "attachments",
"id": "audio_0",
"imgSizes": {
"kqedFullSize": {
"file": "https://ww2.kqed.org/news/wp-content/themes/KQED-unified/img/audio_bgs/background0.jpg"
}
}
},
"audio_1": {
"type": "attachments",
"id": "audio_1",
"imgSizes": {
"kqedFullSize": {
"file": "https://ww2.kqed.org/news/wp-content/themes/KQED-unified/img/audio_bgs/background1.jpg"
}
}
},
"audio_2": {
"type": "attachments",
"id": "audio_2",
"imgSizes": {
"kqedFullSize": {
"file": "https://ww2.kqed.org/news/wp-content/themes/KQED-unified/img/audio_bgs/background2.jpg"
}
}
},
"audio_3": {
"type": "attachments",
"id": "audio_3",
"imgSizes": {
"kqedFullSize": {
"file": "https://ww2.kqed.org/news/wp-content/themes/KQED-unified/img/audio_bgs/background3.jpg"
}
}
},
"audio_4": {
"type": "attachments",
"id": "audio_4",
"imgSizes": {
"kqedFullSize": {
"file": "https://ww2.kqed.org/news/wp-content/themes/KQED-unified/img/audio_bgs/background4.jpg"
}
}
},
"placeholder": {
"type": "attachments",
"id": "placeholder",
"imgSizes": {
"thumbnail": {
"file": "https://cdn.kqed.org/wp-content/uploads/2024/12/KQED-Default-Image-816638274-2000x1333-1-160x107.jpg",
"width": 160,
"height": 107,
"mimeType": "image/jpeg"
},
"medium": {
"file": "https://cdn.kqed.org/wp-content/uploads/2024/12/KQED-Default-Image-816638274-2000x1333-1-800x533.jpg",
"width": 800,
"height": 533,
"mimeType": "image/jpeg"
},
"medium_large": {
"file": "https://cdn.kqed.org/wp-content/uploads/2024/12/KQED-Default-Image-816638274-2000x1333-1-768x512.jpg",
"width": 768,
"height": 512,
"mimeType": "image/jpeg"
},
"large": {
"file": "https://cdn.kqed.org/wp-content/uploads/2024/12/KQED-Default-Image-816638274-2000x1333-1-1020x680.jpg",
"width": 1020,
"height": 680,
"mimeType": "image/jpeg"
},
"1536x1536": {
"file": "https://cdn.kqed.org/wp-content/uploads/2024/12/KQED-Default-Image-816638274-2000x1333-1-1536x1024.jpg",
"width": 1536,
"height": 1024,
"mimeType": "image/jpeg"
},
"fd-lrg": {
"file": "https://cdn.kqed.org/wp-content/uploads/2024/12/KQED-Default-Image-816638274-2000x1333-1-1536x1024.jpg",
"width": 1536,
"height": 1024,
"mimeType": "image/jpeg"
},
"fd-med": {
"file": "https://cdn.kqed.org/wp-content/uploads/2024/12/KQED-Default-Image-816638274-2000x1333-1-1020x680.jpg",
"width": 1020,
"height": 680,
"mimeType": "image/jpeg"
},
"fd-sm": {
"file": "https://cdn.kqed.org/wp-content/uploads/2024/12/KQED-Default-Image-816638274-2000x1333-1-800x533.jpg",
"width": 800,
"height": 533,
"mimeType": "image/jpeg"
},
"post-thumbnail": {
"file": "https://cdn.kqed.org/wp-content/uploads/2024/12/KQED-Default-Image-816638274-2000x1333-1-672x372.jpg",
"width": 672,
"height": 372,
"mimeType": "image/jpeg"
},
"twentyfourteen-full-width": {
"file": "https://cdn.kqed.org/wp-content/uploads/2024/12/KQED-Default-Image-816638274-2000x1333-1-1038x576.jpg",
"width": 1038,
"height": 576,
"mimeType": "image/jpeg"
},
"xxsmall": {
"file": "https://cdn.kqed.org/wp-content/uploads/2024/12/KQED-Default-Image-816638274-2000x1333-1-160x107.jpg",
"width": 160,
"height": 107,
"mimeType": "image/jpeg"
},
"xsmall": {
"file": "https://cdn.kqed.org/wp-content/uploads/2024/12/KQED-Default-Image-816638274-2000x1333-1-672x372.jpg",
"width": 672,
"height": 372,
"mimeType": "image/jpeg"
},
"small": {
"file": "https://cdn.kqed.org/wp-content/uploads/2024/12/KQED-Default-Image-816638274-2000x1333-1-672x372.jpg",
"width": 672,
"height": 372,
"mimeType": "image/jpeg"
},
"xlarge": {
"file": "https://cdn.kqed.org/wp-content/uploads/2024/12/KQED-Default-Image-816638274-2000x1333-1-1020x680.jpg",
"width": 1020,
"height": 680,
"mimeType": "image/jpeg"
},
"full-width": {
"file": "https://cdn.kqed.org/wp-content/uploads/2024/12/KQED-Default-Image-816638274-2000x1333-1-1920x1280.jpg",
"width": 1920,
"height": 1280,
"mimeType": "image/jpeg"
},
"guest-author-32": {
"file": "https://cdn.kqed.org/wp-content/uploads/2025/01/KQED-Default-Image-816638274-1333x1333-1-160x160.jpg",
"width": 32,
"height": 32,
"mimeType": "image/jpeg"
},
"guest-author-50": {
"file": "https://cdn.kqed.org/wp-content/uploads/2025/01/KQED-Default-Image-816638274-1333x1333-1-160x160.jpg",
"width": 50,
"height": 50,
"mimeType": "image/jpeg"
},
"guest-author-64": {
"file": "https://cdn.kqed.org/wp-content/uploads/2025/01/KQED-Default-Image-816638274-1333x1333-1-160x160.jpg",
"width": 64,
"height": 64,
"mimeType": "image/jpeg"
},
"guest-author-96": {
"file": "https://cdn.kqed.org/wp-content/uploads/2025/01/KQED-Default-Image-816638274-1333x1333-1-160x160.jpg",
"width": 96,
"height": 96,
"mimeType": "image/jpeg"
},
"guest-author-128": {
"file": "https://cdn.kqed.org/wp-content/uploads/2025/01/KQED-Default-Image-816638274-1333x1333-1-160x160.jpg",
"width": 128,
"height": 128,
"mimeType": "image/jpeg"
},
"detail": {
"file": "https://cdn.kqed.org/wp-content/uploads/2025/01/KQED-Default-Image-816638274-1333x1333-1-160x160.jpg",
"width": 160,
"height": 160,
"mimeType": "image/jpeg"
},
"kqedFullSize": {
"file": "https://cdn.kqed.org/wp-content/uploads/2024/12/KQED-Default-Image-816638274-2000x1333-1.jpg",
"width": 2000,
"height": 1333
}
}
},
"mindshift_66462": {
"type": "attachments",
"id": "mindshift_66462",
"meta": {
"index": "attachments_1716263798",
"site": "mindshift",
"id": "66462",
"found": true
},
"title": "Bored school boy in class",
"publishDate": 1783917225,
"status": "inherit",
"parent": 66459,
"modified": 1783917250,
"caption": null,
"credit": "Ridofranz/iStock",
"altTag": "Bored student in classroom",
"description": null,
"imgSizes": {
"medium": {
"file": "https://cdn.kqed.org/wp-content/uploads/sites/23/2026/07/iStock-950609084-2000x1328.jpg",
"width": 2000,
"height": 1328,
"mimeType": "image/jpeg"
},
"large": {
"file": "https://cdn.kqed.org/wp-content/uploads/sites/23/2026/07/iStock-950609084-2000x1328.jpg",
"width": 2000,
"height": 1328,
"mimeType": "image/jpeg"
},
"thumbnail": {
"file": "https://cdn.kqed.org/wp-content/uploads/sites/23/2026/07/iStock-950609084-160x106.jpg",
"width": 160,
"height": 106,
"mimeType": "image/jpeg"
},
"medium_large": {
"file": "https://cdn.kqed.org/wp-content/uploads/sites/23/2026/07/iStock-950609084-768x510.jpg",
"width": 768,
"height": 510,
"mimeType": "image/jpeg"
},
"1536x1536": {
"file": "https://cdn.kqed.org/wp-content/uploads/sites/23/2026/07/iStock-950609084-1536x1020.jpg",
"width": 1536,
"height": 1020,
"mimeType": "image/jpeg"
},
"2048x2048": {
"file": "https://cdn.kqed.org/wp-content/uploads/sites/23/2026/07/iStock-950609084-2048x1360.jpg",
"width": 2048,
"height": 1360,
"mimeType": "image/jpeg"
},
"post-thumbnail": {
"file": "https://cdn.kqed.org/wp-content/uploads/sites/23/2026/07/iStock-950609084-672x372.jpg",
"width": 672,
"height": 372,
"mimeType": "image/jpeg"
},
"twentyfourteen-full-width": {
"file": "https://cdn.kqed.org/wp-content/uploads/sites/23/2026/07/iStock-950609084-1038x576.jpg",
"width": 1038,
"height": 576,
"mimeType": "image/jpeg"
},
"full-width": {
"file": "https://cdn.kqed.org/wp-content/uploads/sites/23/2026/07/iStock-950609084-2000x1328.jpg",
"width": 2000,
"height": 1328,
"mimeType": "image/jpeg"
},
"npr-cds-wide": {
"file": "https://cdn.kqed.org/wp-content/uploads/sites/23/2026/07/iStock-950609084-1200x675.jpg",
"width": 1200,
"height": 675,
"mimeType": "image/jpeg"
},
"npr-cds-square": {
"file": "https://cdn.kqed.org/wp-content/uploads/sites/23/2026/07/iStock-950609084-600x600.jpg",
"width": 600,
"height": 600,
"mimeType": "image/jpeg"
},
"kqedFullSize": {
"file": "https://cdn.kqed.org/wp-content/uploads/sites/23/2026/07/iStock-950609084.jpg",
"width": 2125,
"height": 1411
}
},
"isLoading": false,
"fetchFailed": false
},
"mindshift_66422": {
"type": "attachments",
"id": "mindshift_66422",
"meta": {
"index": "attachments_1716263798",
"site": "mindshift",
"id": "66422",
"found": true
},
"title": "Human head with mathematical seamless pattern on abstract blue wall background",
"publishDate": 1783314322,
"status": "inherit",
"parent": 66419,
"modified": 1783314346,
"caption": null,
"credit": "Makhbubakhon Ismatova/iStock",
"altTag": "Human head with mathematical seamless pattern on abstract blue wall background",
"description": null,
"imgSizes": {
"medium": {
"file": "https://cdn.kqed.org/wp-content/uploads/sites/23/2026/07/iStock-1778144903-2000x1333.jpg",
"width": 2000,
"height": 1333,
"mimeType": "image/jpeg"
},
"large": {
"file": "https://cdn.kqed.org/wp-content/uploads/sites/23/2026/07/iStock-1778144903-2000x1333.jpg",
"width": 2000,
"height": 1333,
"mimeType": "image/jpeg"
},
"thumbnail": {
"file": "https://cdn.kqed.org/wp-content/uploads/sites/23/2026/07/iStock-1778144903-160x107.jpg",
"width": 160,
"height": 107,
"mimeType": "image/jpeg"
},
"medium_large": {
"file": "https://cdn.kqed.org/wp-content/uploads/sites/23/2026/07/iStock-1778144903-768x512.jpg",
"width": 768,
"height": 512,
"mimeType": "image/jpeg"
},
"1536x1536": {
"file": "https://cdn.kqed.org/wp-content/uploads/sites/23/2026/07/iStock-1778144903-1536x1024.jpg",
"width": 1536,
"height": 1024,
"mimeType": "image/jpeg"
},
"2048x2048": {
"file": "https://cdn.kqed.org/wp-content/uploads/sites/23/2026/07/iStock-1778144903-2048x1365.jpg",
"width": 2048,
"height": 1365,
"mimeType": "image/jpeg"
},
"post-thumbnail": {
"file": "https://cdn.kqed.org/wp-content/uploads/sites/23/2026/07/iStock-1778144903-672x372.jpg",
"width": 672,
"height": 372,
"mimeType": "image/jpeg"
},
"twentyfourteen-full-width": {
"file": "https://cdn.kqed.org/wp-content/uploads/sites/23/2026/07/iStock-1778144903-1038x576.jpg",
"width": 1038,
"height": 576,
"mimeType": "image/jpeg"
},
"full-width": {
"file": "https://cdn.kqed.org/wp-content/uploads/sites/23/2026/07/iStock-1778144903-2000x1333.jpg",
"width": 2000,
"height": 1333,
"mimeType": "image/jpeg"
},
"npr-cds-wide": {
"file": "https://cdn.kqed.org/wp-content/uploads/sites/23/2026/07/iStock-1778144903-1200x675.jpg",
"width": 1200,
"height": 675,
"mimeType": "image/jpeg"
},
"npr-cds-square": {
"file": "https://cdn.kqed.org/wp-content/uploads/sites/23/2026/07/iStock-1778144903-600x600.jpg",
"width": 600,
"height": 600,
"mimeType": "image/jpeg"
},
"kqedFullSize": {
"file": "https://cdn.kqed.org/wp-content/uploads/sites/23/2026/07/iStock-1778144903.jpg",
"width": 2121,
"height": 1414
}
},
"isLoading": false,
"fetchFailed": false
},
"mindshift_66380": {
"type": "attachments",
"id": "mindshift_66380",
"meta": {
"index": "attachments_1716263798",
"site": "mindshift",
"id": "66380",
"found": true
},
"title": "Audience Full of Tech People in Dark Conference Hall Watching an Innovative Inspiring Keynote Presentation. Business Technology Summit Auditorium Room Crowded with Delegates. Static Shot from Behind.",
"publishDate": 1780701148,
"status": "inherit",
"parent": 66379,
"modified": 1780701182,
"caption": null,
"credit": "gorodenkoff/iStock",
"altTag": "Rear view of audience in a darkened room",
"description": null,
"imgSizes": {
"medium": {
"file": "https://cdn.kqed.org/wp-content/uploads/sites/23/2026/06/iStock-1482843278-2000x1055.jpg",
"width": 2000,
"height": 1055,
"mimeType": "image/jpeg"
},
"large": {
"file": "https://cdn.kqed.org/wp-content/uploads/sites/23/2026/06/iStock-1482843278-2000x1055.jpg",
"width": 2000,
"height": 1055,
"mimeType": "image/jpeg"
},
"thumbnail": {
"file": "https://cdn.kqed.org/wp-content/uploads/sites/23/2026/06/iStock-1482843278-160x84.jpg",
"width": 160,
"height": 84,
"mimeType": "image/jpeg"
},
"medium_large": {
"file": "https://cdn.kqed.org/wp-content/uploads/sites/23/2026/06/iStock-1482843278-768x405.jpg",
"width": 768,
"height": 405,
"mimeType": "image/jpeg"
},
"1536x1536": {
"file": "https://cdn.kqed.org/wp-content/uploads/sites/23/2026/06/iStock-1482843278-1536x810.jpg",
"width": 1536,
"height": 810,
"mimeType": "image/jpeg"
},
"2048x2048": {
"file": "https://cdn.kqed.org/wp-content/uploads/sites/23/2026/06/iStock-1482843278-2048x1080.jpg",
"width": 2048,
"height": 1080,
"mimeType": "image/jpeg"
},
"post-thumbnail": {
"file": "https://cdn.kqed.org/wp-content/uploads/sites/23/2026/06/iStock-1482843278-672x372.jpg",
"width": 672,
"height": 372,
"mimeType": "image/jpeg"
},
"twentyfourteen-full-width": {
"file": "https://cdn.kqed.org/wp-content/uploads/sites/23/2026/06/iStock-1482843278-1038x576.jpg",
"width": 1038,
"height": 576,
"mimeType": "image/jpeg"
},
"full-width": {
"file": "https://cdn.kqed.org/wp-content/uploads/sites/23/2026/06/iStock-1482843278-2000x1055.jpg",
"width": 2000,
"height": 1055,
"mimeType": "image/jpeg"
},
"npr-cds-wide": {
"file": "https://cdn.kqed.org/wp-content/uploads/sites/23/2026/06/iStock-1482843278-1200x675.jpg",
"width": 1200,
"height": 675,
"mimeType": "image/jpeg"
},
"npr-cds-square": {
"file": "https://cdn.kqed.org/wp-content/uploads/sites/23/2026/06/iStock-1482843278-600x600.jpg",
"width": 600,
"height": 600,
"mimeType": "image/jpeg"
},
"kqedFullSize": {
"file": "https://cdn.kqed.org/wp-content/uploads/sites/23/2026/06/iStock-1482843278.jpg",
"width": 2384,
"height": 1257
}
},
"isLoading": false,
"fetchFailed": false
},
"mindshift_66359": {
"type": "attachments",
"id": "mindshift_66359",
"meta": {
"index": "attachments_1716263798",
"site": "mindshift",
"id": "66359",
"found": true
},
"title": "_36A9649.tif.jpg",
"publishDate": 1779469241,
"status": "inherit",
"parent": 66358,
"modified": 1779470541,
"caption": "Mary Acebu, a special education teacher at Riverview Middle School in Bay Point, Calif., works with a student during a math lesson. She says using AI to help with the paperwork part of her job allows her to spend more time interacting with her students in a meaningful way.",
"credit": "Talia Herman for NPR ",
"altTag": "A teacher helps a student who is seated at her desk.",
"description": "Special education teacher Mary Acebu teaching Princess Fernandez Roman during her mixed 7th & 8th grade math class at Riverview Middle School, Bay Point, May 8th, 2026.\n\nSpEd teacher retention is really low, and teachers cite the mounds of paperwork as one of the main reasons it’s hard to do their jobs. They are trying to use AI to speed up the paperwork process so they can spend more time teaching and preparing for classes.",
"imgSizes": {
"medium": {
"file": "https://cdn.kqed.org/wp-content/uploads/sites/23/2026/05/36a9649-tif-2000x1334.jpeg",
"width": 2000,
"height": 1334,
"mimeType": "image/jpeg"
},
"large": {
"file": "https://cdn.kqed.org/wp-content/uploads/sites/23/2026/05/36a9649-tif-2000x1334.jpeg",
"width": 2000,
"height": 1334,
"mimeType": "image/jpeg"
},
"thumbnail": {
"file": "https://cdn.kqed.org/wp-content/uploads/sites/23/2026/05/36a9649-tif-160x107.jpeg",
"width": 160,
"height": 107,
"mimeType": "image/jpeg"
},
"medium_large": {
"file": "https://cdn.kqed.org/wp-content/uploads/sites/23/2026/05/36a9649-tif-768x512.jpeg",
"width": 768,
"height": 512,
"mimeType": "image/jpeg"
},
"1536x1536": {
"file": "https://cdn.kqed.org/wp-content/uploads/sites/23/2026/05/36a9649-tif-1536x1025.jpeg",
"width": 1536,
"height": 1025,
"mimeType": "image/jpeg"
},
"2048x2048": {
"file": "https://cdn.kqed.org/wp-content/uploads/sites/23/2026/05/36a9649-tif-2048x1366.jpeg",
"width": 2048,
"height": 1366,
"mimeType": "image/jpeg"
},
"post-thumbnail": {
"file": "https://cdn.kqed.org/wp-content/uploads/sites/23/2026/05/36a9649-tif-672x372.jpeg",
"width": 672,
"height": 372,
"mimeType": "image/jpeg"
},
"twentyfourteen-full-width": {
"file": "https://cdn.kqed.org/wp-content/uploads/sites/23/2026/05/36a9649-tif-1038x576.jpeg",
"width": 1038,
"height": 576,
"mimeType": "image/jpeg"
},
"full-width": {
"file": "https://cdn.kqed.org/wp-content/uploads/sites/23/2026/05/36a9649-tif-2000x1334.jpeg",
"width": 2000,
"height": 1334,
"mimeType": "image/jpeg"
},
"npr-cds-wide": {
"file": "https://cdn.kqed.org/wp-content/uploads/sites/23/2026/05/36a9649-tif-1200x675.jpeg",
"width": 1200,
"height": 675,
"mimeType": "image/jpeg"
},
"npr-cds-square": {
"file": "https://cdn.kqed.org/wp-content/uploads/sites/23/2026/05/36a9649-tif-600x600.jpeg",
"width": 600,
"height": 600,
"mimeType": "image/jpeg"
},
"kqedFullSize": {
"file": "https://cdn.kqed.org/wp-content/uploads/sites/23/2026/05/36a9649-tif-scaled.jpeg",
"width": 2560,
"height": 1708
}
},
"isLoading": false,
"fetchFailed": false
},
"mindshift_66312": {
"type": "attachments",
"id": "mindshift_66312",
"meta": {
"index": "attachments_1716263798",
"site": "mindshift",
"id": "66312",
"found": true
},
"title": "Close up view on girl's hand doing homework in notebook on wooden table",
"publishDate": 1777655336,
"status": "inherit",
"parent": 66311,
"modified": 1777655490,
"caption": "Federal data suggests that the amount of math homework assigned to fourth and eighth grade students, in particular, has been steadily declining.",
"credit": "Stanislaw Pytel/Getty Images",
"altTag": "Closeup of a student doing math homework. ",
"description": null,
"imgSizes": {
"medium": {
"file": "https://cdn.kqed.org/wp-content/uploads/sites/23/2026/04/k12-math-homework-1-2000x1334.jpeg",
"width": 2000,
"height": 1334,
"mimeType": "image/jpeg"
},
"large": {
"file": "https://cdn.kqed.org/wp-content/uploads/sites/23/2026/04/k12-math-homework-1-2000x1334.jpeg",
"width": 2000,
"height": 1334,
"mimeType": "image/jpeg"
},
"thumbnail": {
"file": "https://cdn.kqed.org/wp-content/uploads/sites/23/2026/04/k12-math-homework-1-160x107.jpeg",
"width": 160,
"height": 107,
"mimeType": "image/jpeg"
},
"medium_large": {
"file": "https://cdn.kqed.org/wp-content/uploads/sites/23/2026/04/k12-math-homework-1-768x512.jpeg",
"width": 768,
"height": 512,
"mimeType": "image/jpeg"
},
"1536x1536": {
"file": "https://cdn.kqed.org/wp-content/uploads/sites/23/2026/04/k12-math-homework-1-1536x1024.jpeg",
"width": 1536,
"height": 1024,
"mimeType": "image/jpeg"
},
"2048x2048": {
"file": "https://cdn.kqed.org/wp-content/uploads/sites/23/2026/04/k12-math-homework-1-2048x1366.jpeg",
"width": 2048,
"height": 1366,
"mimeType": "image/jpeg"
},
"post-thumbnail": {
"file": "https://cdn.kqed.org/wp-content/uploads/sites/23/2026/04/k12-math-homework-1-672x372.jpeg",
"width": 672,
"height": 372,
"mimeType": "image/jpeg"
},
"twentyfourteen-full-width": {
"file": "https://cdn.kqed.org/wp-content/uploads/sites/23/2026/04/k12-math-homework-1-1038x576.jpeg",
"width": 1038,
"height": 576,
"mimeType": "image/jpeg"
},
"full-width": {
"file": "https://cdn.kqed.org/wp-content/uploads/sites/23/2026/04/k12-math-homework-1-2000x1334.jpeg",
"width": 2000,
"height": 1334,
"mimeType": "image/jpeg"
},
"npr-cds-wide": {
"file": "https://cdn.kqed.org/wp-content/uploads/sites/23/2026/04/k12-math-homework-1-1200x675.jpeg",
"width": 1200,
"height": 675,
"mimeType": "image/jpeg"
},
"npr-cds-square": {
"file": "https://cdn.kqed.org/wp-content/uploads/sites/23/2026/04/k12-math-homework-1-600x600.jpeg",
"width": 600,
"height": 600,
"mimeType": "image/jpeg"
},
"kqedFullSize": {
"file": "https://cdn.kqed.org/wp-content/uploads/sites/23/2026/04/k12-math-homework-1-scaled.jpeg",
"width": 2560,
"height": 1707
}
},
"isLoading": false,
"fetchFailed": false
},
"mindshift_66300": {
"type": "attachments",
"id": "mindshift_66300",
"meta": {
"index": "attachments_1716263798",
"site": "mindshift",
"id": "66300",
"found": true
},
"title": "Design Students Working On Computers In CAD/3D Printing Lab",
"publishDate": 1777272076,
"status": "inherit",
"parent": 66299,
"modified": 1777272112,
"caption": null,
"credit": "monkeybusinessimages/iStock",
"altTag": "Students working on computers",
"description": null,
"imgSizes": {
"medium": {
"file": "https://cdn.kqed.org/wp-content/uploads/sites/23/2026/04/iStock-875672414-2000x1333.jpg",
"width": 2000,
"height": 1333,
"mimeType": "image/jpeg"
},
"large": {
"file": "https://cdn.kqed.org/wp-content/uploads/sites/23/2026/04/iStock-875672414-2000x1333.jpg",
"width": 2000,
"height": 1333,
"mimeType": "image/jpeg"
},
"thumbnail": {
"file": "https://cdn.kqed.org/wp-content/uploads/sites/23/2026/04/iStock-875672414-160x107.jpg",
"width": 160,
"height": 107,
"mimeType": "image/jpeg"
},
"medium_large": {
"file": "https://cdn.kqed.org/wp-content/uploads/sites/23/2026/04/iStock-875672414-768x512.jpg",
"width": 768,
"height": 512,
"mimeType": "image/jpeg"
},
"1536x1536": {
"file": "https://cdn.kqed.org/wp-content/uploads/sites/23/2026/04/iStock-875672414-1536x1024.jpg",
"width": 1536,
"height": 1024,
"mimeType": "image/jpeg"
},
"2048x2048": {
"file": "https://cdn.kqed.org/wp-content/uploads/sites/23/2026/04/iStock-875672414-2048x1365.jpg",
"width": 2048,
"height": 1365,
"mimeType": "image/jpeg"
},
"post-thumbnail": {
"file": "https://cdn.kqed.org/wp-content/uploads/sites/23/2026/04/iStock-875672414-672x372.jpg",
"width": 672,
"height": 372,
"mimeType": "image/jpeg"
},
"twentyfourteen-full-width": {
"file": "https://cdn.kqed.org/wp-content/uploads/sites/23/2026/04/iStock-875672414-1038x576.jpg",
"width": 1038,
"height": 576,
"mimeType": "image/jpeg"
},
"full-width": {
"file": "https://cdn.kqed.org/wp-content/uploads/sites/23/2026/04/iStock-875672414-2000x1333.jpg",
"width": 2000,
"height": 1333,
"mimeType": "image/jpeg"
},
"npr-cds-wide": {
"file": "https://cdn.kqed.org/wp-content/uploads/sites/23/2026/04/iStock-875672414-1200x675.jpg",
"width": 1200,
"height": 675,
"mimeType": "image/jpeg"
},
"npr-cds-square": {
"file": "https://cdn.kqed.org/wp-content/uploads/sites/23/2026/04/iStock-875672414-600x600.jpg",
"width": 600,
"height": 600,
"mimeType": "image/jpeg"
},
"kqedFullSize": {
"file": "https://cdn.kqed.org/wp-content/uploads/sites/23/2026/04/iStock-875672414.jpg",
"width": 2121,
"height": 1414
}
},
"isLoading": false,
"fetchFailed": false
},
"mindshift_66290": {
"type": "attachments",
"id": "mindshift_66290",
"meta": {
"index": "attachments_1716263798",
"site": "mindshift",
"id": "66290",
"found": true
},
"title": "Composite trend artwork sketch image photo collage of hand wrist sleeve appear hole gesture clapping tablet display cooperation applause",
"publishDate": 1777056066,
"status": "inherit",
"parent": 66289,
"modified": 1777056250,
"caption": null,
"credit": "Deagreez / iStockphoto ",
"altTag": "collage of hands clapping",
"description": "Composite trend artwork sketch image photo collage of hand wrist sleeve appear hole gesture clapping tablet display cooperation applause.",
"imgSizes": {
"thumbnail": {
"file": "https://cdn.kqed.org/wp-content/uploads/sites/23/2026/04/gettyimages-2182063851-160x120.jpeg",
"width": 160,
"height": 120,
"mimeType": "image/jpeg"
},
"medium_large": {
"file": "https://cdn.kqed.org/wp-content/uploads/sites/23/2026/04/gettyimages-2182063851-768x576.jpeg",
"width": 768,
"height": 576,
"mimeType": "image/jpeg"
},
"1536x1536": {
"file": "https://cdn.kqed.org/wp-content/uploads/sites/23/2026/04/gettyimages-2182063851-1536x1152.jpeg",
"width": 1536,
"height": 1152,
"mimeType": "image/jpeg"
},
"post-thumbnail": {
"file": "https://cdn.kqed.org/wp-content/uploads/sites/23/2026/04/gettyimages-2182063851-672x372.jpeg",
"width": 672,
"height": 372,
"mimeType": "image/jpeg"
},
"twentyfourteen-full-width": {
"file": "https://cdn.kqed.org/wp-content/uploads/sites/23/2026/04/gettyimages-2182063851-1038x576.jpeg",
"width": 1038,
"height": 576,
"mimeType": "image/jpeg"
},
"npr-cds-wide": {
"file": "https://cdn.kqed.org/wp-content/uploads/sites/23/2026/04/gettyimages-2182063851-1200x675.jpeg",
"width": 1200,
"height": 675,
"mimeType": "image/jpeg"
},
"npr-cds-square": {
"file": "https://cdn.kqed.org/wp-content/uploads/sites/23/2026/04/gettyimages-2182063851-600x600.jpeg",
"width": 600,
"height": 600,
"mimeType": "image/jpeg"
},
"kqedFullSize": {
"file": "https://cdn.kqed.org/wp-content/uploads/sites/23/2026/04/gettyimages-2182063851.jpeg",
"width": 1718,
"height": 1288
}
},
"isLoading": false,
"fetchFailed": false
},
"mindshift_66240": {
"type": "attachments",
"id": "mindshift_66240",
"meta": {
"index": "attachments_1716263798",
"site": "mindshift",
"id": "66240",
"found": true
},
"title": "proof-ai-tutor-feat-scaled",
"publishDate": 1775458285,
"status": "inherit",
"parent": 66237,
"modified": 1775458302,
"caption": null,
"credit": "Getty Images",
"altTag": null,
"description": null,
"imgSizes": {
"medium": {
"file": "https://cdn.kqed.org/wp-content/uploads/sites/23/2026/04/proof-ai-tutor-feat-scaled-1-2000x1334.jpg",
"width": 2000,
"height": 1334,
"mimeType": "image/jpeg"
},
"large": {
"file": "https://cdn.kqed.org/wp-content/uploads/sites/23/2026/04/proof-ai-tutor-feat-scaled-1-2000x1334.jpg",
"width": 2000,
"height": 1334,
"mimeType": "image/jpeg"
},
"thumbnail": {
"file": "https://cdn.kqed.org/wp-content/uploads/sites/23/2026/04/proof-ai-tutor-feat-scaled-1-160x107.jpg",
"width": 160,
"height": 107,
"mimeType": "image/jpeg"
},
"medium_large": {
"file": "https://cdn.kqed.org/wp-content/uploads/sites/23/2026/04/proof-ai-tutor-feat-scaled-1-768x512.jpg",
"width": 768,
"height": 512,
"mimeType": "image/jpeg"
},
"1536x1536": {
"file": "https://cdn.kqed.org/wp-content/uploads/sites/23/2026/04/proof-ai-tutor-feat-scaled-1-1536x1024.jpg",
"width": 1536,
"height": 1024,
"mimeType": "image/jpeg"
},
"2048x2048": {
"file": "https://cdn.kqed.org/wp-content/uploads/sites/23/2026/04/proof-ai-tutor-feat-scaled-1-2048x1366.jpg",
"width": 2048,
"height": 1366,
"mimeType": "image/jpeg"
},
"post-thumbnail": {
"file": "https://cdn.kqed.org/wp-content/uploads/sites/23/2026/04/proof-ai-tutor-feat-scaled-1-672x372.jpg",
"width": 672,
"height": 372,
"mimeType": "image/jpeg"
},
"twentyfourteen-full-width": {
"file": "https://cdn.kqed.org/wp-content/uploads/sites/23/2026/04/proof-ai-tutor-feat-scaled-1-1038x576.jpg",
"width": 1038,
"height": 576,
"mimeType": "image/jpeg"
},
"full-width": {
"file": "https://cdn.kqed.org/wp-content/uploads/sites/23/2026/04/proof-ai-tutor-feat-scaled-1-2000x1334.jpg",
"width": 2000,
"height": 1334,
"mimeType": "image/jpeg"
},
"npr-cds-wide": {
"file": "https://cdn.kqed.org/wp-content/uploads/sites/23/2026/04/proof-ai-tutor-feat-scaled-1-1200x675.jpg",
"width": 1200,
"height": 675,
"mimeType": "image/jpeg"
},
"npr-cds-square": {
"file": "https://cdn.kqed.org/wp-content/uploads/sites/23/2026/04/proof-ai-tutor-feat-scaled-1-600x600.jpg",
"width": 600,
"height": 600,
"mimeType": "image/jpeg"
},
"kqedFullSize": {
"file": "https://cdn.kqed.org/wp-content/uploads/sites/23/2026/04/proof-ai-tutor-feat-scaled-1.jpg",
"width": 2560,
"height": 1707
}
},
"isLoading": false,
"fetchFailed": false
},
"mindshift_66220": {
"type": "attachments",
"id": "mindshift_66220",
"meta": {
"index": "attachments_1716263798",
"site": "mindshift",
"id": "66220",
"found": true
},
"title": "illustration of man reflecting himself in the mirror, loop surreal concept",
"publishDate": 1774202458,
"status": "inherit",
"parent": 66217,
"modified": 1774202494,
"caption": null,
"credit": "francescoch/iStock",
"altTag": "Surreal illustration of man reflecting himself in the mirror on a loop",
"description": null,
"imgSizes": {
"medium": {
"file": "https://cdn.kqed.org/wp-content/uploads/sites/23/2026/03/iStock-1313859236-2000x1333.jpg",
"width": 2000,
"height": 1333,
"mimeType": "image/jpeg"
},
"large": {
"file": "https://cdn.kqed.org/wp-content/uploads/sites/23/2026/03/iStock-1313859236-2000x1333.jpg",
"width": 2000,
"height": 1333,
"mimeType": "image/jpeg"
},
"thumbnail": {
"file": "https://cdn.kqed.org/wp-content/uploads/sites/23/2026/03/iStock-1313859236-160x107.jpg",
"width": 160,
"height": 107,
"mimeType": "image/jpeg"
},
"medium_large": {
"file": "https://cdn.kqed.org/wp-content/uploads/sites/23/2026/03/iStock-1313859236-768x512.jpg",
"width": 768,
"height": 512,
"mimeType": "image/jpeg"
},
"1536x1536": {
"file": "https://cdn.kqed.org/wp-content/uploads/sites/23/2026/03/iStock-1313859236-1536x1024.jpg",
"width": 1536,
"height": 1024,
"mimeType": "image/jpeg"
},
"2048x2048": {
"file": "https://cdn.kqed.org/wp-content/uploads/sites/23/2026/03/iStock-1313859236-2048x1365.jpg",
"width": 2048,
"height": 1365,
"mimeType": "image/jpeg"
},
"post-thumbnail": {
"file": "https://cdn.kqed.org/wp-content/uploads/sites/23/2026/03/iStock-1313859236-672x372.jpg",
"width": 672,
"height": 372,
"mimeType": "image/jpeg"
},
"twentyfourteen-full-width": {
"file": "https://cdn.kqed.org/wp-content/uploads/sites/23/2026/03/iStock-1313859236-1038x576.jpg",
"width": 1038,
"height": 576,
"mimeType": "image/jpeg"
},
"full-width": {
"file": "https://cdn.kqed.org/wp-content/uploads/sites/23/2026/03/iStock-1313859236-2000x1333.jpg",
"width": 2000,
"height": 1333,
"mimeType": "image/jpeg"
},
"npr-cds-wide": {
"file": "https://cdn.kqed.org/wp-content/uploads/sites/23/2026/03/iStock-1313859236-1200x675.jpg",
"width": 1200,
"height": 675,
"mimeType": "image/jpeg"
},
"kqedFullSize": {
"file": "https://cdn.kqed.org/wp-content/uploads/sites/23/2026/03/iStock-1313859236.jpg",
"width": 2121,
"height": 1414
}
},
"isLoading": false,
"fetchFailed": false
}
},
"audioPlayerReducer": {
"postId": "stream_live",
"isPaused": true,
"isPlaying": false,
"pfsActive": false,
"pledgeModalIsOpen": true,
"playerDrawerIsOpen": false,
"liveAudioPlayStartedAt": 0,
"liveAudioPlayContext": ""
},
"authorsReducer": {
"byline_mindshift_66459": {
"type": "authors",
"id": "byline_mindshift_66459",
"meta": {
"override": true
},
"slug": "byline_mindshift_66459",
"name": "Jill Barshay, \u003ca href=\"https://hechingerreport.org/\">The Hechinger Report\u003c/a>",
"isLoading": false
},
"byline_mindshift_66419": {
"type": "authors",
"id": "byline_mindshift_66419",
"meta": {
"override": true
},
"slug": "byline_mindshift_66419",
"name": "Jill Barshay, \u003ca href=\"https://hechingerreport.org/\">The Hechinger Report\u003c/a>",
"isLoading": false
},
"byline_mindshift_66379": {
"type": "authors",
"id": "byline_mindshift_66379",
"meta": {
"override": true
},
"slug": "byline_mindshift_66379",
"name": "Kristin Fasiang and Jill Barshay, \u003ca href=\"https://hechingerreport.org/\">The Hechinger Report\u003c/a>",
"isLoading": false
},
"byline_mindshift_66358": {
"type": "authors",
"id": "byline_mindshift_66358",
"meta": {
"override": true
},
"slug": "byline_mindshift_66358",
"name": "Jonaki Mehta",
"isLoading": false
},
"byline_mindshift_66311": {
"type": "authors",
"id": "byline_mindshift_66311",
"meta": {
"override": true
},
"slug": "byline_mindshift_66311",
"name": "Ariel Gilreath, \u003ca href=\"https://hechingerreport.org/\">The Hechinger Report\u003c/a>",
"isLoading": false
},
"byline_mindshift_66299": {
"type": "authors",
"id": "byline_mindshift_66299",
"meta": {
"override": true
},
"slug": "byline_mindshift_66299",
"name": "Jill Barshay, \u003ca href=\"https://hechingerreport.org/\" >The Hechinger Report\u003c/a>",
"isLoading": false
},
"byline_mindshift_66289": {
"type": "authors",
"id": "byline_mindshift_66289",
"meta": {
"override": true
},
"slug": "byline_mindshift_66289",
"name": "Ari Daniel",
"isLoading": false
},
"byline_mindshift_66237": {
"type": "authors",
"id": "byline_mindshift_66237",
"meta": {
"override": true
},
"slug": "byline_mindshift_66237",
"name": "Jill Barshay, \u003ca href=\"https://hechingerreport.org/\">The Hechinger Report\u003c/a>",
"isLoading": false
},
"byline_mindshift_66217": {
"type": "authors",
"id": "byline_mindshift_66217",
"meta": {
"override": true
},
"slug": "byline_mindshift_66217",
"name": "Jill Barshay, \u003ca href=\"https://hechingerreport.org/\">The Hechinger Report\u003c/a>",
"isLoading": false
}
},
"pagesReducer": {
"mindshift_tag_artificial-intelligence": {
"type": "terms",
"id": "mindshift_1023",
"meta": {
"index": "terms_1716263798",
"site": "mindshift",
"id": "1023",
"score": 8.554707
},
"featImg": null,
"name": "artificial intelligence",
"description": null,
"taxonomy": "tag",
"headData": {
"twImgId": null,
"twTitle": null,
"ogTitle": null,
"ogImgId": null,
"twDescription": null,
"description": null,
"title": "artificial intelligence Archives | KQED Mindshift",
"ogDescription": null
},
"ttid": 1028,
"slug": "artificial-intelligence",
"isLoading": false,
"title": "artificial intelligence",
"pageMeta": {
"site": "mindshift",
"WpPageTemplate": "page-topic-editorial"
},
"blocks": [
{
"blockName": "kqed/post-list",
"attrs": {
"layout": "cardArticle2",
"query": "posts/mindshift?tag=artificial-intelligence",
"seeMore": true
}
},
{
"blockName": "kqed/ad"
}
]
}
},
"pfsSessionReducer": {},
"postsReducer": {
"stream_live": {
"type": "live",
"id": "stream_live",
"audioUrl": "https://streams.kqed.org/kqedradio",
"title": "Live Stream",
"excerpt": "Live Stream information currently unavailable.",
"link": "/radio",
"featImg": "",
"label": {
"name": "KQED Live",
"link": "/"
}
},
"stream_kqedNewscast": {
"type": "posts",
"id": "stream_kqedNewscast",
"audioUrl": "https://www.kqed.org/.stream/anon/radio/RDnews/newscast.mp3?_=1",
"title": "KQED Newscast",
"featImg": "",
"label": {
"name": "88.5 FM",
"link": "/"
}
},
"mindshift_66459": {
"type": "posts",
"id": "mindshift_66459",
"meta": {
"index": "posts_1716263798",
"site": "mindshift",
"id": "66459",
"score": null,
"sort": [
1783936830000
]
},
"guestAuthors": [],
"slug": "less-interesting-less-enjoyable-why-ai-assisted-classes-feel-less-important",
"title": "Less Interesting, Less Enjoyable: Why AI-Assisted Classes Feel Less Important",
"publishDate": 1783936830,
"format": "standard",
"headTitle": "Less Interesting, Less Enjoyable: Why AI-Assisted Classes Feel Less Important | KQED",
"labelTerm": {
"term": 21847,
"site": "mindshift"
},
"content": "\u003cp>Artificial intelligence is often promoted as a way to make teachers more effective by helping them write lesson plans, generate classroom materials and provide feedback to students in seconds. But one of the first randomized trials testing AI in real classrooms found that it can also undermine learning. Students whose teachers were given access to an AI teaching assistant felt less motivated to learn.\u003c/p>\n\u003cp>The damage was especially pronounced among students whose teachers were already weaker instructors, as measured by their performance before the experiment began. Their students also scored lower on standardized final exams, the researchers found.\u003c/p>\n\u003cp>“Teachers, just like students or coders, might be using AI as a crutch,” said Alp Sungu, lead author of the study and an assistant professor at the Wharton School at the University of Pennsylvania. “Instead of doing the actual work, they’re using AI to delegate the task, and that lowers the quality of their teaching.”\u003c/p>\n\u003cp>A draft of the study, “\u003ca href=\"https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7007339\">Generative AI Can Harm Teaching\u003c/a>,” was released online in June and has not yet been published in a peer-reviewed journal. It echoes Sungu’s widely discussed 2024 research on how \u003ca href=\"https://hechingerreport.org/kids-chatgpt-worse-on-tests/\">students’ use of AI is harming learning\u003c/a>.\u003c/p>\n\u003cp>“Students use AI as an answer machine, not as a tool for learning, and therefore it harms learning,” said Sungu. “Here, I think teachers are potentially using AI as a material generating machine for homework, lecture notes, lesson plans, syllabus. Instead of improving their own output, they’re using AI as a replacement with very minimal interaction, and therefore the quality of output is not good enough.”\u003c/p>\n\u003cp>[ad fullwidth]\u003c/p>\n\u003cp>Sungu’s experiment, conducted with fellow University of Pennsylvania researchers, including educational psychologist Angela Duckworth, followed 193 teachers and more than 2,800 middle and high school students in a private school chain in Turkey during the spring of 2025.\u003c/p>\n\u003cp>Teachers were randomly assigned either to receive access to a ChatGPT-based teaching assistant customized to Turkey’s national curriculum or to continue teaching as usual. Over 10 weeks, teachers primarily used the tool to generate lecture notes, assignments and exams.\u003c/p>\n\u003cp>Students whose teachers had access to the AI tool rated their classes as less enjoyable, less interesting and less important than students in the control group. The decline in intrinsic motivation was modest, but larger among students of those teachers who had already been heavier AI users before the experiment began.\u003c/p>\n\u003cp>Average academic achievement did not change overall. But among teachers whose students had lower marks before the experiment — a proxy for lower-performing teachers — student achievement and confidence both declined. Academic achievement was measured through externally administered standardized exams, ruling out the possibility that these teachers had different grading standards.\u003c/p>\n\u003cp>The study cannot explain exactly why teaching quality deteriorated. Researchers did not observe classrooms or analyze the AI-generated materials teachers used. But Sungu suspects that teachers may have been giving up one of their most effective tools.\u003c/p>\n\u003cp>“When you start using AI-generated material, you’re losing your personal voice,” said Sungu. “It might be technically good enough, but it doesn’t really carry your own style. If everything is very uniform, it just becomes a bit more boring.”\u003c/p>\n\u003cp>One possible explanation for the weaker academic performance among students of low-performing teachers, Sungu said, is that stronger teachers treat AI output as a first draft, revising and adapting it to their classrooms. Weaker teachers, he suspects, may be more likely to use AI-generated material as is.\u003c/p>\n\u003cp>This study is not a clean comparison between teaching with and without AI. Teachers in the control group were free to use other AI tools, making this a comparison between access to a customized AI assistant and whatever teachers chose to do on their own. If anything, Sungu said, these findings might be understating the risks of teachers relying heavily on AI-generated materials.\u003c/p>\n\u003cp>Still, Sungu cautions that it would be a mistake to conclude that “AI is terrible and will ruin education.” He sees a different lesson: Access to AI technology alone does not improve teaching.\u003c/p>\n\u003cp>The challenge is to help teachers use AI in ways that preserve human judgment and creativity. That will require teacher training programs, guardrails and better interfaces.\u003c/p>\n\u003cp>“As of right now, how teachers are using it organically, there is something to be worried about,” he said.\u003c/p>\n\u003cp>Sungu says he personally uses AI in his university teaching to create interactive games and polls that would otherwise take too long to build. “When I first get the output, it just looks great,” he said. “And then, if I don’t immerse myself in it, the examples, the numbers don’t make sense. I end up spending an equal amount of time to improve the output or calibrate it to my class.”\u003c/p>\n\u003cp>“It’s not a time saver,” he said.\u003c/p>\n\u003cp>\u003c/p>\n\u003cp>\u003cem>This story about \u003c/em>\u003ca href=\"https://hechingerreport.org/proof-points-ai-in-teaching/\">\u003cem>AI in teaching\u003c/em>\u003c/a>\u003cem> was produced by \u003c/em>\u003ca href=\"https://hechingerreport.org/special-reports/higher-education/\">The Hechinger Report\u003c/a>\u003cem>, a nonprofit, independent news organization that covers education. Sign up for \u003c/em>\u003ca href=\"https://hechingerreport.org/proofpoints/\">\u003cem>Proof Points\u003c/em>\u003c/a>\u003cem> and other \u003c/em>\u003ca href=\"https://hechingerreport.org/newsletters/\">\u003cem>Hechinger newsletters\u003c/em>\u003c/a>\u003cem>.\u003c/em>\u003c/p>\n\n",
"blocks": [],
"excerpt": "Teachers save time with AI. Their students may pay the price. ",
"status": "publish",
"parent": 0,
"modified": 1783917279,
"stats": {
"hasAudio": false,
"hasVideo": false,
"hasChartOrMap": false,
"iframeSrcs": [],
"hasGoogleForm": false,
"hasGallery": false,
"hasHearkenModule": false,
"hasPolis": false,
"paragraphCount": 21,
"wordCount": 867
},
"headData": {
"title": "Less Interesting, Less Enjoyable: Why AI-Assisted Classes Feel Less Important | KQED",
"description": "Teachers save time with AI. Their students may pay the price. ",
"ogTitle": "",
"ogDescription": "",
"ogImgId": "",
"twTitle": "",
"twDescription": "",
"twImgId": "",
"schema": {
"@context": "https://schema.org",
"@type": "Article",
"headline": "Less Interesting, Less Enjoyable: Why AI-Assisted Classes Feel Less Important",
"datePublished": "2026-07-13T03:00:30-07:00",
"dateModified": "2026-07-12T21:34:39-07:00",
"image": "https://cdn.kqed.org/wp-content/uploads/2020/02/KQED-OG-Image@1x.png"
}
},
"primaryCategory": {
"termId": 21504,
"slug": "education-research",
"name": "Education research"
},
"sticky": false,
"nprByline": "Jill Barshay, \u003ca href=\"https://hechingerreport.org/\">The Hechinger Report\u003c/a>",
"templateType": "standard",
"featuredImageType": "standard",
"excludeFromSiteSearch": "Include",
"showOnAuthorArchivePages": "No",
"articleAge": "0",
"path": "/mindshift/66459/less-interesting-less-enjoyable-why-ai-assisted-classes-feel-less-important",
"audioTrackLength": null,
"parsedContent": [
{
"type": "contentString",
"content": "\u003cdiv class=\"post-body\">\u003cp>\u003cp>Artificial intelligence is often promoted as a way to make teachers more effective by helping them write lesson plans, generate classroom materials and provide feedback to students in seconds. But one of the first randomized trials testing AI in real classrooms found that it can also undermine learning. Students whose teachers were given access to an AI teaching assistant felt less motivated to learn.\u003c/p>\n\u003cp>The damage was especially pronounced among students whose teachers were already weaker instructors, as measured by their performance before the experiment began. Their students also scored lower on standardized final exams, the researchers found.\u003c/p>\n\u003cp>“Teachers, just like students or coders, might be using AI as a crutch,” said Alp Sungu, lead author of the study and an assistant professor at the Wharton School at the University of Pennsylvania. “Instead of doing the actual work, they’re using AI to delegate the task, and that lowers the quality of their teaching.”\u003c/p>\n\u003cp>A draft of the study, “\u003ca href=\"https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7007339\">Generative AI Can Harm Teaching\u003c/a>,” was released online in June and has not yet been published in a peer-reviewed journal. It echoes Sungu’s widely discussed 2024 research on how \u003ca href=\"https://hechingerreport.org/kids-chatgpt-worse-on-tests/\">students’ use of AI is harming learning\u003c/a>.\u003c/p>\n\u003cp>“Students use AI as an answer machine, not as a tool for learning, and therefore it harms learning,” said Sungu. “Here, I think teachers are potentially using AI as a material generating machine for homework, lecture notes, lesson plans, syllabus. Instead of improving their own output, they’re using AI as a replacement with very minimal interaction, and therefore the quality of output is not good enough.”\u003c/p>\n\u003cp>\u003c/p>\u003c/div>",
"attributes": {
"named": {},
"numeric": []
}
},
{
"type": "component",
"content": "",
"name": "ad",
"attributes": {
"named": {
"label": "fullwidth"
},
"numeric": [
"fullwidth"
]
}
},
{
"type": "contentString",
"content": "\u003cdiv class=\"post-body\">\u003cp>\u003c/p>\n\u003cp>Sungu’s experiment, conducted with fellow University of Pennsylvania researchers, including educational psychologist Angela Duckworth, followed 193 teachers and more than 2,800 middle and high school students in a private school chain in Turkey during the spring of 2025.\u003c/p>\n\u003cp>Teachers were randomly assigned either to receive access to a ChatGPT-based teaching assistant customized to Turkey’s national curriculum or to continue teaching as usual. Over 10 weeks, teachers primarily used the tool to generate lecture notes, assignments and exams.\u003c/p>\n\u003cp>Students whose teachers had access to the AI tool rated their classes as less enjoyable, less interesting and less important than students in the control group. The decline in intrinsic motivation was modest, but larger among students of those teachers who had already been heavier AI users before the experiment began.\u003c/p>\n\u003cp>Average academic achievement did not change overall. But among teachers whose students had lower marks before the experiment — a proxy for lower-performing teachers — student achievement and confidence both declined. Academic achievement was measured through externally administered standardized exams, ruling out the possibility that these teachers had different grading standards.\u003c/p>\n\u003cp>The study cannot explain exactly why teaching quality deteriorated. Researchers did not observe classrooms or analyze the AI-generated materials teachers used. But Sungu suspects that teachers may have been giving up one of their most effective tools.\u003c/p>\n\u003cp>“When you start using AI-generated material, you’re losing your personal voice,” said Sungu. “It might be technically good enough, but it doesn’t really carry your own style. If everything is very uniform, it just becomes a bit more boring.”\u003c/p>\n\u003cp>One possible explanation for the weaker academic performance among students of low-performing teachers, Sungu said, is that stronger teachers treat AI output as a first draft, revising and adapting it to their classrooms. Weaker teachers, he suspects, may be more likely to use AI-generated material as is.\u003c/p>\n\u003cp>This study is not a clean comparison between teaching with and without AI. Teachers in the control group were free to use other AI tools, making this a comparison between access to a customized AI assistant and whatever teachers chose to do on their own. If anything, Sungu said, these findings might be understating the risks of teachers relying heavily on AI-generated materials.\u003c/p>\n\u003cp>Still, Sungu cautions that it would be a mistake to conclude that “AI is terrible and will ruin education.” He sees a different lesson: Access to AI technology alone does not improve teaching.\u003c/p>\n\u003cp>The challenge is to help teachers use AI in ways that preserve human judgment and creativity. That will require teacher training programs, guardrails and better interfaces.\u003c/p>\n\u003cp>“As of right now, how teachers are using it organically, there is something to be worried about,” he said.\u003c/p>\n\u003cp>Sungu says he personally uses AI in his university teaching to create interactive games and polls that would otherwise take too long to build. “When I first get the output, it just looks great,” he said. “And then, if I don’t immerse myself in it, the examples, the numbers don’t make sense. I end up spending an equal amount of time to improve the output or calibrate it to my class.”\u003c/p>\n\u003cp>“It’s not a time saver,” he said.\u003c/p>\n\u003cp>\u003c/p>\n\u003cp>\u003cem>This story about \u003c/em>\u003ca href=\"https://hechingerreport.org/proof-points-ai-in-teaching/\">\u003cem>AI in teaching\u003c/em>\u003c/a>\u003cem> was produced by \u003c/em>\u003ca href=\"https://hechingerreport.org/special-reports/higher-education/\">The Hechinger Report\u003c/a>\u003cem>, a nonprofit, independent news organization that covers education. Sign up for \u003c/em>\u003ca href=\"https://hechingerreport.org/proofpoints/\">\u003cem>Proof Points\u003c/em>\u003c/a>\u003cem> and other \u003c/em>\u003ca href=\"https://hechingerreport.org/newsletters/\">\u003cem>Hechinger newsletters\u003c/em>\u003c/a>\u003cem>.\u003c/em>\u003c/p>\n\n\u003c/div>\u003c/p>",
"attributes": {
"named": {},
"numeric": []
}
}
],
"link": "/mindshift/66459/less-interesting-less-enjoyable-why-ai-assisted-classes-feel-less-important",
"authors": [
"byline_mindshift_66459"
],
"programs": [
"mindshift_21847"
],
"categories": [
"mindshift_21504"
],
"tags": [
"mindshift_1023",
"mindshift_21469",
"mindshift_20616",
"mindshift_963"
],
"featImg": "mindshift_66462",
"label": "mindshift_21847"
},
"mindshift_66419": {
"type": "posts",
"id": "mindshift_66419",
"meta": {
"index": "posts_1716263798",
"site": "mindshift",
"id": "66419",
"score": null,
"sort": [
1783332022000
]
},
"guestAuthors": [],
"slug": "cognitive-surrender-faster-solutions-lower-test-scores-show-how-ai-is-eroding-math-skills",
"title": "'Cognitive Surrender': Faster Solutions, Lower Test Scores Show How AI is Eroding Math Skills",
"publishDate": 1783332022,
"format": "standard",
"headTitle": "‘Cognitive Surrender’: Faster Solutions, Lower Test Scores Show How AI is Eroding Math Skills | KQED",
"labelTerm": {
"term": 21847,
"site": "mindshift"
},
"content": "\u003cp>When ChatGPT arrived in late 2022, educators quickly asked whether students would use artificial intelligence to cheat, learn or simply get through homework more efficiently. Evidence is beginning to point toward a troubling answer: Many students appear to be completing assignments faster while learning less from them.\u003c/p>\n\u003cp>This conclusion comes from one of the largest studies of how generative AI is changing student behavior and academic skills. Sina Rismanchian, a doctoral student at the University of California, Irvine, partnered with researchers at McGraw Hill to \u003ca href=\"https://arxiv.org/pdf/2605.21629\">analyze millions of student interactions with ALEKS\u003c/a>, an online math platform used by more than four million students a year, from fifth grade through college. Because ALEKS includes both low-stakes practice problems and college placement tests, the researchers were able to compare how students behaved and performed before and after ChatGPT’s arrival.\u003c/p>\n\u003cp>To isolate AI’s effects, the researchers compared two kinds of math problems that differ in how easily students can outsource them to AI: word problems and graphing problems.\u003c/p>\n\u003cp>Word problems can be copied and pasted directly into AI chatbots for instant answers. Graphing problems are far more cumbersome. A student would need to upload a screenshot and still recreate the graph inside ALEKS using its tools.\u003c/p>\n\u003cp>After ChatGPT’s launch, student behavior and performance on the two types of problems began to diverge.\u003c/p>\n\u003cp>[ad fullwidth]\u003c/p>\n\u003cp>Beginning in early 2023, students started spending less time on word problems while continuing to spend about the same amount of time on graphing problems. The gap widened every quarter. By the end of the study period, near the end of 2025, average time spent on word problems had fallen 31 percent among high school students and 27 percent among college students — from about four minutes per word problem to less than three. (Middle school students showed only a modest decline of 9 percent, and fifth graders showed essentially none.)\u003c/p>\n\u003cp>The researchers believe those averages are being pulled downward by some students who spend only seconds on word problems because they’re using AI to answer them.\u003c/p>\n\u003cp>The same pattern appeared in college placement tests. When the exams were taken without supervision, students spent much less time on word problems after ChatGPT’s release. During proctored exams, the time spent on word problems returned to historical norms.\u003c/p>\n\u003cp>But time is only half the story. The more troubling finding is what happened to learning.\u003c/p>\n\u003cp>Many colleges allow incoming students to retake placement tests after practicing more math in ALEKS, giving them a chance to qualify for a higher-level course. Before ChatGPT, that practice generally paid off. After ChatGPT, students answered more word problems correctly during unsupervised practice sessions but performed substantially worse on those same kinds of problems when they later took a proctored placement test.\u003c/p>\n\u003cp>Historically, students answered about 80 percent of these word problems correctly on supervised placement tests. After ChatGPT’s introduction, that fell to about 60 percent — a roughly 25 percent reduction in the odds of answering a word problem correctly.\u003c/p>\n\u003cp>Performance on graphing problems, by contrast, did not decline.\u003c/p>\n\u003cp>\u003cstrong>After ChatGPT’s release, students performed worse on word problems (AI-susceptible) during proctored exams, but answer more word problems correctly in nonproctored settings\u003cbr>\n\u003c/strong>\u003c/p>\n\u003cfigure id=\"attachment_66423\" class=\"wp-caption alignnone\" style=\"max-width: 512px\">\u003cimg loading=\"lazy\" decoding=\"async\" class=\"wp-image-66423 size-full\" src=\"https://cdn.kqed.org/wp-content/uploads/sites/23/2026/07/Rismanchian-et-al.png\" alt=\"Graph showing score gaps in exams. \" width=\"512\" height=\"186\" srcset=\"https://cdn.kqed.org/wp-content/uploads/sites/23/2026/07/Rismanchian-et-al.png 512w, https://cdn.kqed.org/wp-content/uploads/sites/23/2026/07/Rismanchian-et-al-160x58.png 160w\" sizes=\"auto, (max-width: 512px) 100vw, 512px\">\u003cfigcaption class=\"wp-caption-text\">The dotted line marks the public release of ChatGPT. Source: Figure 4, Rismanchian et al “Faster Completion, Less Learning: Generative AI Reduced Study Time on Math Problems and the Knowledge They Build,” June 2026 preprint.\u003c/figcaption>\u003c/figure>\n\u003cp>If students’ math skills had generally deteriorated because of pandemic learning loss, weaker high school preparation or digital distraction, graphing performance should have deteriorated too. It didn’t.\u003c/p>\n\u003cp>The study cannot definitively prove that students were using AI. The researchers couldn’t see what else was happening on students’ screens outside of ALEKS. But it’s difficult to think of another explanation. The changes appeared only in problems that are easy to outsource to AI, disappeared under supervision and grew steadily over nearly three years.\u003c/p>\n\u003cp>“What makes me nervous is that it’s not only about the word problems,” Rismanchian told me. “This cognitive surrender might be going on in writing, science, everything.”\u003c/p>\n\u003cp>The paper, “\u003ca href=\"https://arxiv.org/pdf/2605.21629\">Faster Completion, Less Learning\u003c/a>,” was released in June 2026 as a working paper and has not yet been peer reviewed. Like any single study, it doesn’t settle the questions of how much students are using AI in their schoolwork, whether it’s harming learning and by how much. But it joins a growing body of evidence that generative AI is causing students to skip the brain work that leads to learning, and that this “cognitive surrender” is becoming commonplace.\u003c/p>\n\u003cp>A randomized experiment in Turkey found that high school students who used AI to help them study math ultimately \u003ca href=\"https://hechingerreport.org/kids-chatgpt-worse-on-tests/\">learned less\u003c/a> than students who practiced without it. Anthropic, the maker of Claude, has separately reported that many college students appear to use AI to obtain answers and \u003ca href=\"https://hechingerreport.org/proof-points-offload-critical-thinking-ai/\">offload cognitive work\u003c/a>. Rismanchian’s earlier research, released in March 2026, documented \u003ca href=\"https://www.researchgate.net/publication/403013405_Artificial_Integrity_Concerning_Patterns_of_AI_Usage_Among_Undergraduate_Students\">troubling patterns of AI usage\u003c/a> in short response essays among undergraduate students at a large California research university.\u003c/p>\n\u003cp>That doesn’t mean AI always undermines learning. Carefully designed AI tutors have improved student achievement in controlled experiments by asking questions, personalizing instruction and withholding answers until students reason their way through a problem. But using AI this way should increase the time students spend on a problem, Rismanchian said. The ALEKS data show the opposite.\u003c/p>\n\u003cp>Rismanchian doesn’t believe the answer is simply banning AI. Instead, he argues, students need to value learning enough to resist the temptation to outsource it.\u003c/p>\n\u003cp>A recent RAND survey suggests many already recognize the threat to their brains. Students report worrying that AI is \u003ca href=\"https://www.rand.org/pubs/research_reports/RRA4742-1.html\">weakening their critical-thinking skills\u003c/a> while more of them admit using it for schoolwork.\u003c/p>\n\u003cp>Students are not entirely to blame. Even as many professors have warned students not to use AI to complete classwork, universities themselves have embraced the technology, often giving students free access to premium chatbots.\u003c/p>\n\u003cp>“I think we need to communicate to students that you should value your learning,” Rismanchian said. “If ChatGPT does it for you, then you haven’t learned it.”\u003c/p>\n\u003cp>Rismanchian understands the temptation.\u003c/p>\n\u003cp>An international student, Rismanchian began using ChatGPT to help polish the English in his papers. The ideas were still his own. But after several months, he said, he noticed something unsettling.\u003c/p>\n\u003cp>“I realized that I cannot write anymore,” he said. “I was losing my writing abilities.”\u003c/p>\n\u003cp>So he stopped using AI to write.\u003c/p>\n\u003cp>He still uses it to code.\u003c/p>\n\u003cp>[ad floatright]\u003c/p>\n\u003cp>\u003cem>This story about \u003c/em>\u003ca href=\"https://hechingerreport.org/proof-points-ai-eroding-math-skills/\">\u003cem>AI use eroding math skills\u003c/em>\u003c/a>\u003cem> was produced by \u003c/em>\u003ca href=\"https://hechingerreport.org/special-reports/higher-education/\">The Hechinger Report\u003c/a>\u003cem>, a nonprofit, independent news organization that covers education. Sign up for \u003c/em>\u003ca href=\"https://hechingerreport.org/proofpoints/\">\u003cem>Proof Points\u003c/em>\u003c/a>\u003cem> and other \u003c/em>\u003ca href=\"https://hechingerreport.org/newsletters/\">\u003cem>Hechinger newsletters\u003c/em>\u003c/a>\u003cem>.\u003c/em>\u003c/p>\n\n",
"blocks": [],
"excerpt": "AI can be a helpful tool to help students learn, but many are taking shortcuts and learning less, according to a study. ",
"status": "publish",
"parent": 0,
"modified": 1783314608,
"stats": {
"hasAudio": false,
"hasVideo": false,
"hasChartOrMap": false,
"iframeSrcs": [],
"hasGoogleForm": false,
"hasGallery": false,
"hasHearkenModule": false,
"hasPolis": false,
"paragraphCount": 31,
"wordCount": 1153
},
"headData": {
"title": "'Cognitive Surrender': Faster Solutions, Lower Test Scores Show How AI is Eroding Math Skills | KQED",
"description": "AI can be a helpful tool to help students learn, but many are taking shortcuts and learning less, according to a study. ",
"ogTitle": "",
"ogDescription": "",
"ogImgId": "",
"twTitle": "",
"twDescription": "",
"twImgId": "",
"schema": {
"@context": "https://schema.org",
"@type": "Article",
"headline": "'Cognitive Surrender': Faster Solutions, Lower Test Scores Show How AI is Eroding Math Skills",
"datePublished": "2026-07-06T03:00:22-07:00",
"dateModified": "2026-07-05T22:10:08-07:00",
"image": "https://cdn.kqed.org/wp-content/uploads/2020/02/KQED-OG-Image@1x.png"
}
},
"primaryCategory": {
"termId": 21504,
"slug": "education-research",
"name": "Education research"
},
"sticky": false,
"nprByline": "Jill Barshay, \u003ca href=\"https://hechingerreport.org/\">The Hechinger Report\u003c/a>",
"templateType": "standard",
"featuredImageType": "standard",
"excludeFromSiteSearch": "Include",
"showOnAuthorArchivePages": "No",
"articleAge": "0",
"path": "/mindshift/66419/cognitive-surrender-faster-solutions-lower-test-scores-show-how-ai-is-eroding-math-skills",
"audioTrackLength": null,
"parsedContent": [
{
"type": "contentString",
"content": "\u003cdiv class=\"post-body\">\u003cp>\u003cp>When ChatGPT arrived in late 2022, educators quickly asked whether students would use artificial intelligence to cheat, learn or simply get through homework more efficiently. Evidence is beginning to point toward a troubling answer: Many students appear to be completing assignments faster while learning less from them.\u003c/p>\n\u003cp>This conclusion comes from one of the largest studies of how generative AI is changing student behavior and academic skills. Sina Rismanchian, a doctoral student at the University of California, Irvine, partnered with researchers at McGraw Hill to \u003ca href=\"https://arxiv.org/pdf/2605.21629\">analyze millions of student interactions with ALEKS\u003c/a>, an online math platform used by more than four million students a year, from fifth grade through college. Because ALEKS includes both low-stakes practice problems and college placement tests, the researchers were able to compare how students behaved and performed before and after ChatGPT’s arrival.\u003c/p>\n\u003cp>To isolate AI’s effects, the researchers compared two kinds of math problems that differ in how easily students can outsource them to AI: word problems and graphing problems.\u003c/p>\n\u003cp>Word problems can be copied and pasted directly into AI chatbots for instant answers. Graphing problems are far more cumbersome. A student would need to upload a screenshot and still recreate the graph inside ALEKS using its tools.\u003c/p>\n\u003cp>After ChatGPT’s launch, student behavior and performance on the two types of problems began to diverge.\u003c/p>\n\u003cp>\u003c/p>\u003c/div>",
"attributes": {
"named": {},
"numeric": []
}
},
{
"type": "component",
"content": "",
"name": "ad",
"attributes": {
"named": {
"label": "fullwidth"
},
"numeric": [
"fullwidth"
]
}
},
{
"type": "contentString",
"content": "\u003cdiv class=\"post-body\">\u003cp>\u003c/p>\n\u003cp>Beginning in early 2023, students started spending less time on word problems while continuing to spend about the same amount of time on graphing problems. The gap widened every quarter. By the end of the study period, near the end of 2025, average time spent on word problems had fallen 31 percent among high school students and 27 percent among college students — from about four minutes per word problem to less than three. (Middle school students showed only a modest decline of 9 percent, and fifth graders showed essentially none.)\u003c/p>\n\u003cp>The researchers believe those averages are being pulled downward by some students who spend only seconds on word problems because they’re using AI to answer them.\u003c/p>\n\u003cp>The same pattern appeared in college placement tests. When the exams were taken without supervision, students spent much less time on word problems after ChatGPT’s release. During proctored exams, the time spent on word problems returned to historical norms.\u003c/p>\n\u003cp>But time is only half the story. The more troubling finding is what happened to learning.\u003c/p>\n\u003cp>Many colleges allow incoming students to retake placement tests after practicing more math in ALEKS, giving them a chance to qualify for a higher-level course. Before ChatGPT, that practice generally paid off. After ChatGPT, students answered more word problems correctly during unsupervised practice sessions but performed substantially worse on those same kinds of problems when they later took a proctored placement test.\u003c/p>\n\u003cp>Historically, students answered about 80 percent of these word problems correctly on supervised placement tests. After ChatGPT’s introduction, that fell to about 60 percent — a roughly 25 percent reduction in the odds of answering a word problem correctly.\u003c/p>\n\u003cp>Performance on graphing problems, by contrast, did not decline.\u003c/p>\n\u003cp>\u003cstrong>After ChatGPT’s release, students performed worse on word problems (AI-susceptible) during proctored exams, but answer more word problems correctly in nonproctored settings\u003cbr>\n\u003c/strong>\u003c/p>\n\u003cfigure id=\"attachment_66423\" class=\"wp-caption alignnone\" style=\"max-width: 512px\">\u003cimg loading=\"lazy\" decoding=\"async\" class=\"wp-image-66423 size-full\" src=\"https://cdn.kqed.org/wp-content/uploads/sites/23/2026/07/Rismanchian-et-al.png\" alt=\"Graph showing score gaps in exams. \" width=\"512\" height=\"186\" srcset=\"https://cdn.kqed.org/wp-content/uploads/sites/23/2026/07/Rismanchian-et-al.png 512w, https://cdn.kqed.org/wp-content/uploads/sites/23/2026/07/Rismanchian-et-al-160x58.png 160w\" sizes=\"auto, (max-width: 512px) 100vw, 512px\">\u003cfigcaption class=\"wp-caption-text\">The dotted line marks the public release of ChatGPT. Source: Figure 4, Rismanchian et al “Faster Completion, Less Learning: Generative AI Reduced Study Time on Math Problems and the Knowledge They Build,” June 2026 preprint.\u003c/figcaption>\u003c/figure>\n\u003cp>If students’ math skills had generally deteriorated because of pandemic learning loss, weaker high school preparation or digital distraction, graphing performance should have deteriorated too. It didn’t.\u003c/p>\n\u003cp>The study cannot definitively prove that students were using AI. The researchers couldn’t see what else was happening on students’ screens outside of ALEKS. But it’s difficult to think of another explanation. The changes appeared only in problems that are easy to outsource to AI, disappeared under supervision and grew steadily over nearly three years.\u003c/p>\n\u003cp>“What makes me nervous is that it’s not only about the word problems,” Rismanchian told me. “This cognitive surrender might be going on in writing, science, everything.”\u003c/p>\n\u003cp>The paper, “\u003ca href=\"https://arxiv.org/pdf/2605.21629\">Faster Completion, Less Learning\u003c/a>,” was released in June 2026 as a working paper and has not yet been peer reviewed. Like any single study, it doesn’t settle the questions of how much students are using AI in their schoolwork, whether it’s harming learning and by how much. But it joins a growing body of evidence that generative AI is causing students to skip the brain work that leads to learning, and that this “cognitive surrender” is becoming commonplace.\u003c/p>\n\u003cp>A randomized experiment in Turkey found that high school students who used AI to help them study math ultimately \u003ca href=\"https://hechingerreport.org/kids-chatgpt-worse-on-tests/\">learned less\u003c/a> than students who practiced without it. Anthropic, the maker of Claude, has separately reported that many college students appear to use AI to obtain answers and \u003ca href=\"https://hechingerreport.org/proof-points-offload-critical-thinking-ai/\">offload cognitive work\u003c/a>. Rismanchian’s earlier research, released in March 2026, documented \u003ca href=\"https://www.researchgate.net/publication/403013405_Artificial_Integrity_Concerning_Patterns_of_AI_Usage_Among_Undergraduate_Students\">troubling patterns of AI usage\u003c/a> in short response essays among undergraduate students at a large California research university.\u003c/p>\n\u003cp>That doesn’t mean AI always undermines learning. Carefully designed AI tutors have improved student achievement in controlled experiments by asking questions, personalizing instruction and withholding answers until students reason their way through a problem. But using AI this way should increase the time students spend on a problem, Rismanchian said. The ALEKS data show the opposite.\u003c/p>\n\u003cp>Rismanchian doesn’t believe the answer is simply banning AI. Instead, he argues, students need to value learning enough to resist the temptation to outsource it.\u003c/p>\n\u003cp>A recent RAND survey suggests many already recognize the threat to their brains. Students report worrying that AI is \u003ca href=\"https://www.rand.org/pubs/research_reports/RRA4742-1.html\">weakening their critical-thinking skills\u003c/a> while more of them admit using it for schoolwork.\u003c/p>\n\u003cp>Students are not entirely to blame. Even as many professors have warned students not to use AI to complete classwork, universities themselves have embraced the technology, often giving students free access to premium chatbots.\u003c/p>\n\u003cp>“I think we need to communicate to students that you should value your learning,” Rismanchian said. “If ChatGPT does it for you, then you haven’t learned it.”\u003c/p>\n\u003cp>Rismanchian understands the temptation.\u003c/p>\n\u003cp>An international student, Rismanchian began using ChatGPT to help polish the English in his papers. The ideas were still his own. But after several months, he said, he noticed something unsettling.\u003c/p>\n\u003cp>“I realized that I cannot write anymore,” he said. “I was losing my writing abilities.”\u003c/p>\n\u003cp>So he stopped using AI to write.\u003c/p>\n\u003cp>He still uses it to code.\u003c/p>\n\u003cp>\u003c/p>\u003c/div>",
"attributes": {
"named": {},
"numeric": []
}
},
{
"type": "component",
"content": "",
"name": "ad",
"attributes": {
"named": {
"label": "floatright"
},
"numeric": [
"floatright"
]
}
},
{
"type": "contentString",
"content": "\u003cdiv class=\"post-body\">\u003cp>\u003c/p>\n\u003cp>\u003cem>This story about \u003c/em>\u003ca href=\"https://hechingerreport.org/proof-points-ai-eroding-math-skills/\">\u003cem>AI use eroding math skills\u003c/em>\u003c/a>\u003cem> was produced by \u003c/em>\u003ca href=\"https://hechingerreport.org/special-reports/higher-education/\">The Hechinger Report\u003c/a>\u003cem>, a nonprofit, independent news organization that covers education. Sign up for \u003c/em>\u003ca href=\"https://hechingerreport.org/proofpoints/\">\u003cem>Proof Points\u003c/em>\u003c/a>\u003cem> and other \u003c/em>\u003ca href=\"https://hechingerreport.org/newsletters/\">\u003cem>Hechinger newsletters\u003c/em>\u003c/a>\u003cem>.\u003c/em>\u003c/p>\n\n\u003c/div>\u003c/p>",
"attributes": {
"named": {},
"numeric": []
}
}
],
"link": "/mindshift/66419/cognitive-surrender-faster-solutions-lower-test-scores-show-how-ai-is-eroding-math-skills",
"authors": [
"byline_mindshift_66419"
],
"programs": [
"mindshift_21847"
],
"categories": [
"mindshift_21504"
],
"tags": [
"mindshift_1023",
"mindshift_21078",
"mindshift_843",
"mindshift_20893",
"mindshift_21949"
],
"featImg": "mindshift_66422",
"label": "mindshift_21847"
},
"mindshift_66379": {
"type": "posts",
"id": "mindshift_66379",
"meta": {
"index": "posts_1716263798",
"site": "mindshift",
"id": "66379",
"score": null,
"sort": [
1780912851000
]
},
"guestAuthors": [],
"slug": "is-everyone-using-ai-how-false-perceptions-can-become-self-fulfilling",
"title": "Is Everyone Using AI? How False Perceptions Can Become Self-fulfilling",
"publishDate": 1780912851,
"format": "standard",
"headTitle": "Is Everyone Using AI? How False Perceptions Can Become Self-fulfilling | KQED",
"labelTerm": {
"term": 21847,
"site": "mindshift"
},
"content": "\u003cp>As colleges scramble to write rules for artificial intelligence in the classroom, one basic question remains unknown: How many students are actually using it?\u003c/p>\n\u003cp>\u003cspan style=\"font-weight: 400;\">An anonymous survey of 338 undergraduates at the University of Chicago shows that the answer may be hard to pin down — not just because AI use is changing quickly, but because students may not be self-reporting it accurately.\u003c/span>\u003c/p>\n\u003cp>\u003cspan style=\"font-weight: 400;\">In the survey, 60 percent of students said they personally use AI tools such as ChatGPT. But 90 percent said they believed the average student on campus uses AI. \u003c/span>\u003c/p>\n\u003cp>\u003cspan style=\"font-weight: 400;\">That 30-point gap could mean that students are underreporting their own AI use, overestimating their peers’ use, or both. Without reliable information about how many students are using AI and how they are using it, college administrators risk designing policies based on assumptions rather than evidence. \u003c/span>\u003c/p>\n\u003cp>\u003cspan style=\"font-weight: 400;\">The University of Chicago researchers behind the survey suspect that college students aren’t being truthful about their actual use of AI because they’re ashamed. \u003c/span>\u003c/p>\n\u003cp>[ad fullwidth]\u003c/p>\n\u003cp>\u003cspan style=\"font-weight: 400;\">“Students don’t want to be perceived by their peers as not able to do the work,” said Alex Kale, a computer scientist at the University of Chicago and a co-author of the \u003c/span>\u003ca href=\"https://dl.acm.org/doi/10.1145/3772318.3791073\">\u003cspan style=\"font-weight: 400;\">study\u003c/span>\u003c/a>\u003cspan style=\"font-weight: 400;\">, which was presented at a conference in Barcelona, Spain, in April. “They don’t want to be perceived by their peers as dishonest … And it feels deeply personal.”\u003c/span>\u003c/p>\n\u003cp>\u003cspan style=\"font-weight: 400;\">Kale calls this phenomenon “social desirability bias,” the human tendency to answer questions in a way that makes us look good to others (and to ourselves), rather than being completely honest, even in an anonymous survey. In a separate online survey of 98 undergraduates conducted by the researchers, respondents said that admitting to using AI was akin to admitting that you’re “not able to complete coursework independently,” or are “lazy.” Another respondent thought that students were hiding usage for fear of getting caught and possibly expelled.\u003c/span>\u003c/p>\n\u003cp>\u003cspan style=\"font-weight: 400;\">The researchers offer an alternate explanation for the gap. Students may be overestimating how many of their peers are using AI because it is such a visible part of campus life. They hear people talking about ChatGPT. They see AI tools open on laptop screens. That can start to feel like the norm. One survey respondent expressed it like this: “I think only a small portion of students actually rely on LLMs to do coursework, while most students do not. That small portion leads some students to assume most are using it.” (The current post-2022 generation of AI tools like ChatGPT are often referred to as large language models or LLMs.)\u003c/span>\u003c/p>\n\u003cp>\u003cspan style=\"font-weight: 400;\">In other words, students may be using AI more than they admit, while AI hype may also be creating the impression that everyone is using it. \u003c/span>\u003c/p>\n\u003cp>\u003cspan style=\"font-weight: 400;\">This same phenomenon — a big gap between what students admit to doing and what they believe their peers are doing — is commonly found in public health research on \u003c/span>\u003ca href=\"https://pubmed.ncbi.nlm.nih.gov/10368559/\">\u003cspan style=\"font-weight: 400;\">alcohol, drugs\u003c/span>\u003c/a>\u003cspan style=\"font-weight: 400;\"> and \u003c/span>\u003ca href=\"https://academic.oup.com/ijpor/article-abstract/20/1/52/738000?redirectedFrom=fulltext&login=false&utm_source=chatgpt.com\">\u003cspan style=\"font-weight: 400;\">sex\u003c/span>\u003c/a>\u003cspan style=\"font-weight: 400;\">. Students often overestimate how much their peers drink heavily, use drugs or engage in casual sex. And that has had big implications for curbing unhealthy behaviors. When students believe that “everyone else is doing it,” they are more likely to engage in it too. The false perception becomes partly self-fulfilling.\u003c/span>\u003c/p>\n\u003cp>\u003cspan style=\"font-weight: 400;\">More than 25 years ago, colleges began to worry that warning students about binge drinking on campus was backfiring and actually encouraging students to get drunk. Many \u003c/span>\u003ca href=\"https://www.nytimes.com/2000/10/03/us/new-tactic-on-college-drinking-play-it-down.html\">\u003cspan style=\"font-weight: 400;\">shifted strategy\u003c/span>\u003c/a>\u003cspan style=\"font-weight: 400;\">, downplaying the problem of binge drinking and publicizing statistics that most students drink in moderation. The number of students who said they drink heavily declined, according to some public health officials. \u003c/span>\u003c/p>\n\u003cp>\u003cspan style=\"font-weight: 400;\">There may be some lessons here for how to encourage the responsible use of AI, even though the University of Chicago study doesn’t link the AI use to drugs or booze. But it does raise the point that perceptions matter. If students believe that nearly everyone is relying on AI to complete coursework, they may feel pressure to use it themselves just to keep up.\u003c/span>\u003c/p>\n\u003cp>\u003ci>\u003cspan style=\"font-weight: 400;\">Kristin Fasiang is a graduate student in computer science and learning sciences at Northwestern University. Fasiang reported and wrote this story along with The Hechinger Report’s Jill Barshay.\u003c/span>\u003c/i>\u003c/p>\n\u003cp>\u003c/p>\n\u003cp>\u003ci>\u003cspan style=\"font-weight: 400;\">This story about \u003c/span>\u003c/i>\u003ca href=\"https://hechingerreport.org/proof-points-ai-use-college-campuses/\">\u003ci>\u003cspan style=\"font-weight: 400;\">AI use on college campuses\u003c/span>\u003c/i>\u003c/a>\u003ci>\u003cspan style=\"font-weight: 400;\"> was produced by \u003c/span>\u003c/i>\u003ca href=\"https://hechingerreport.org/special-reports/higher-education/\">\u003cspan style=\"font-weight: 400;\">The Hechinger Report\u003c/span>\u003c/a>\u003ci>\u003cspan style=\"font-weight: 400;\">, a nonprofit, independent news organization that covers education. Sign up for \u003c/span>\u003c/i>\u003ca href=\"https://hechingerreport.org/proofpoints/\">\u003ci>\u003cspan style=\"font-weight: 400;\">Proof Points\u003c/span>\u003c/i>\u003c/a>\u003ci>\u003cspan style=\"font-weight: 400;\"> and other \u003c/span>\u003c/i>\u003ca href=\"https://hechingerreport.org/newsletters/\">\u003ci>\u003cspan style=\"font-weight: 400;\">Hechinger newsletters\u003c/span>\u003c/i>\u003c/a>\u003ci>\u003cspan style=\"font-weight: 400;\">.\u003c/span>\u003c/i>\u003c/p>\n\n",
"blocks": [],
"excerpt": "Researchers say a lack of reliable information on artificial intelligence use on campus could lead to misguided policies.",
"status": "publish",
"parent": 0,
"modified": 1780701266,
"stats": {
"hasAudio": false,
"hasVideo": false,
"hasChartOrMap": false,
"iframeSrcs": [],
"hasGoogleForm": false,
"hasGallery": false,
"hasHearkenModule": false,
"hasPolis": false,
"paragraphCount": 16,
"wordCount": 755
},
"headData": {
"title": "Is Everyone Using AI? How False Perceptions Can Become Self-fulfilling | KQED",
"description": "Researchers say a lack of reliable information on artificial intelligence use on campus could lead to misguided policies.",
"ogTitle": "",
"ogDescription": "",
"ogImgId": "",
"twTitle": "",
"twDescription": "",
"twImgId": "",
"schema": {
"@context": "https://schema.org",
"@type": "Article",
"headline": "Is Everyone Using AI? How False Perceptions Can Become Self-fulfilling",
"datePublished": "2026-06-08T03:00:51-07:00",
"dateModified": "2026-06-05T16:14:26-07:00",
"image": "https://cdn.kqed.org/wp-content/uploads/2020/02/KQED-OG-Image@1x.png"
}
},
"primaryCategory": {
"termId": 21504,
"slug": "education-research",
"name": "Education research"
},
"sticky": false,
"nprByline": "Kristin Fasiang and Jill Barshay, \u003ca href=\"https://hechingerreport.org/\">The Hechinger Report\u003c/a>",
"templateType": "standard",
"featuredImageType": "standard",
"excludeFromSiteSearch": "Include",
"showOnAuthorArchivePages": "No",
"articleAge": "0",
"path": "/mindshift/66379/is-everyone-using-ai-how-false-perceptions-can-become-self-fulfilling",
"audioTrackLength": null,
"parsedContent": [
{
"type": "contentString",
"content": "\u003cdiv class=\"post-body\">\u003cp>\u003cp>As colleges scramble to write rules for artificial intelligence in the classroom, one basic question remains unknown: How many students are actually using it?\u003c/p>\n\u003cp>\u003cspan style=\"font-weight: 400;\">An anonymous survey of 338 undergraduates at the University of Chicago shows that the answer may be hard to pin down — not just because AI use is changing quickly, but because students may not be self-reporting it accurately.\u003c/span>\u003c/p>\n\u003cp>\u003cspan style=\"font-weight: 400;\">In the survey, 60 percent of students said they personally use AI tools such as ChatGPT. But 90 percent said they believed the average student on campus uses AI. \u003c/span>\u003c/p>\n\u003cp>\u003cspan style=\"font-weight: 400;\">That 30-point gap could mean that students are underreporting their own AI use, overestimating their peers’ use, or both. Without reliable information about how many students are using AI and how they are using it, college administrators risk designing policies based on assumptions rather than evidence. \u003c/span>\u003c/p>\n\u003cp>\u003cspan style=\"font-weight: 400;\">The University of Chicago researchers behind the survey suspect that college students aren’t being truthful about their actual use of AI because they’re ashamed. \u003c/span>\u003c/p>\n\u003cp>\u003c/p>\u003c/div>",
"attributes": {
"named": {},
"numeric": []
}
},
{
"type": "component",
"content": "",
"name": "ad",
"attributes": {
"named": {
"label": "fullwidth"
},
"numeric": [
"fullwidth"
]
}
},
{
"type": "contentString",
"content": "\u003cdiv class=\"post-body\">\u003cp>\u003c/p>\n\u003cp>\u003cspan style=\"font-weight: 400;\">“Students don’t want to be perceived by their peers as not able to do the work,” said Alex Kale, a computer scientist at the University of Chicago and a co-author of the \u003c/span>\u003ca href=\"https://dl.acm.org/doi/10.1145/3772318.3791073\">\u003cspan style=\"font-weight: 400;\">study\u003c/span>\u003c/a>\u003cspan style=\"font-weight: 400;\">, which was presented at a conference in Barcelona, Spain, in April. “They don’t want to be perceived by their peers as dishonest … And it feels deeply personal.”\u003c/span>\u003c/p>\n\u003cp>\u003cspan style=\"font-weight: 400;\">Kale calls this phenomenon “social desirability bias,” the human tendency to answer questions in a way that makes us look good to others (and to ourselves), rather than being completely honest, even in an anonymous survey. In a separate online survey of 98 undergraduates conducted by the researchers, respondents said that admitting to using AI was akin to admitting that you’re “not able to complete coursework independently,” or are “lazy.” Another respondent thought that students were hiding usage for fear of getting caught and possibly expelled.\u003c/span>\u003c/p>\n\u003cp>\u003cspan style=\"font-weight: 400;\">The researchers offer an alternate explanation for the gap. Students may be overestimating how many of their peers are using AI because it is such a visible part of campus life. They hear people talking about ChatGPT. They see AI tools open on laptop screens. That can start to feel like the norm. One survey respondent expressed it like this: “I think only a small portion of students actually rely on LLMs to do coursework, while most students do not. That small portion leads some students to assume most are using it.” (The current post-2022 generation of AI tools like ChatGPT are often referred to as large language models or LLMs.)\u003c/span>\u003c/p>\n\u003cp>\u003cspan style=\"font-weight: 400;\">In other words, students may be using AI more than they admit, while AI hype may also be creating the impression that everyone is using it. \u003c/span>\u003c/p>\n\u003cp>\u003cspan style=\"font-weight: 400;\">This same phenomenon — a big gap between what students admit to doing and what they believe their peers are doing — is commonly found in public health research on \u003c/span>\u003ca href=\"https://pubmed.ncbi.nlm.nih.gov/10368559/\">\u003cspan style=\"font-weight: 400;\">alcohol, drugs\u003c/span>\u003c/a>\u003cspan style=\"font-weight: 400;\"> and \u003c/span>\u003ca href=\"https://academic.oup.com/ijpor/article-abstract/20/1/52/738000?redirectedFrom=fulltext&login=false&utm_source=chatgpt.com\">\u003cspan style=\"font-weight: 400;\">sex\u003c/span>\u003c/a>\u003cspan style=\"font-weight: 400;\">. Students often overestimate how much their peers drink heavily, use drugs or engage in casual sex. And that has had big implications for curbing unhealthy behaviors. When students believe that “everyone else is doing it,” they are more likely to engage in it too. The false perception becomes partly self-fulfilling.\u003c/span>\u003c/p>\n\u003cp>\u003cspan style=\"font-weight: 400;\">More than 25 years ago, colleges began to worry that warning students about binge drinking on campus was backfiring and actually encouraging students to get drunk. Many \u003c/span>\u003ca href=\"https://www.nytimes.com/2000/10/03/us/new-tactic-on-college-drinking-play-it-down.html\">\u003cspan style=\"font-weight: 400;\">shifted strategy\u003c/span>\u003c/a>\u003cspan style=\"font-weight: 400;\">, downplaying the problem of binge drinking and publicizing statistics that most students drink in moderation. The number of students who said they drink heavily declined, according to some public health officials. \u003c/span>\u003c/p>\n\u003cp>\u003cspan style=\"font-weight: 400;\">There may be some lessons here for how to encourage the responsible use of AI, even though the University of Chicago study doesn’t link the AI use to drugs or booze. But it does raise the point that perceptions matter. If students believe that nearly everyone is relying on AI to complete coursework, they may feel pressure to use it themselves just to keep up.\u003c/span>\u003c/p>\n\u003cp>\u003ci>\u003cspan style=\"font-weight: 400;\">Kristin Fasiang is a graduate student in computer science and learning sciences at Northwestern University. Fasiang reported and wrote this story along with The Hechinger Report’s Jill Barshay.\u003c/span>\u003c/i>\u003c/p>\n\u003cp>\u003c/p>\n\u003cp>\u003ci>\u003cspan style=\"font-weight: 400;\">This story about \u003c/span>\u003c/i>\u003ca href=\"https://hechingerreport.org/proof-points-ai-use-college-campuses/\">\u003ci>\u003cspan style=\"font-weight: 400;\">AI use on college campuses\u003c/span>\u003c/i>\u003c/a>\u003ci>\u003cspan style=\"font-weight: 400;\"> was produced by \u003c/span>\u003c/i>\u003ca href=\"https://hechingerreport.org/special-reports/higher-education/\">\u003cspan style=\"font-weight: 400;\">The Hechinger Report\u003c/span>\u003c/a>\u003ci>\u003cspan style=\"font-weight: 400;\">, a nonprofit, independent news organization that covers education. Sign up for \u003c/span>\u003c/i>\u003ca href=\"https://hechingerreport.org/proofpoints/\">\u003ci>\u003cspan style=\"font-weight: 400;\">Proof Points\u003c/span>\u003c/i>\u003c/a>\u003ci>\u003cspan style=\"font-weight: 400;\"> and other \u003c/span>\u003c/i>\u003ca href=\"https://hechingerreport.org/newsletters/\">\u003ci>\u003cspan style=\"font-weight: 400;\">Hechinger newsletters\u003c/span>\u003c/i>\u003c/a>\u003ci>\u003cspan style=\"font-weight: 400;\">.\u003c/span>\u003c/i>\u003c/p>\n\n\u003c/div>\u003c/p>",
"attributes": {
"named": {},
"numeric": []
}
}
],
"link": "/mindshift/66379/is-everyone-using-ai-how-false-perceptions-can-become-self-fulfilling",
"authors": [
"byline_mindshift_66379"
],
"programs": [
"mindshift_21847"
],
"categories": [
"mindshift_21504"
],
"tags": [
"mindshift_1023",
"mindshift_739",
"mindshift_68"
],
"featImg": "mindshift_66380",
"label": "mindshift_21847"
},
"mindshift_66358": {
"type": "posts",
"id": "mindshift_66358",
"meta": {
"index": "posts_1716263798",
"site": "mindshift",
"id": "66358",
"score": null,
"sort": [
1779296451000
]
},
"guestAuthors": [],
"slug": "overworked-and-understaffed-special-ed-teachers-turn-to-ai-for-help",
"title": "Overworked and Understaffed: Special Ed Teachers Turn to AI for Help",
"publishDate": 1779296451,
"format": "standard",
"headTitle": "Overworked and Understaffed: Special Ed Teachers Turn to AI for Help | KQED",
"labelTerm": {
"term": 21847,
"site": "mindshift"
},
"content": "\u003cp>\u003cem>Editor’s note: NPR uses only the first names of minors in this story because it discusses their learning disabilities and placement in special education.\u003c/em>\u003c/p>\n\u003cp>BAY POINT, Calif. — The sun would just be rising when teacher Mary Acebu began her days. She’d blast music on the way to work to get energized and get to her classroom by 6:30 to prepare for her students’ arrival at 8. Often, it’d be dark by the time she headed home, sometimes with paperwork in tow.\u003c/p>\n\u003cp>Like so many special education teachers around the country, this was Acebu’s life for much of the 10 years she’s been teaching at Riverview Middle School, in this small, unincorporated northern California town.\u003c/p>\n\u003cp>“I don’t do that anymore,” she says with a laugh.\u003c/p>\n\u003cp>That’s because Acebu has been experimenting with artificial intelligence for the last two years to get through paperwork more quickly and says it’s helped her instead use precious time for student interaction. “I have time to talk to the kiddos and really build those relationships,” she says, “instead of sitting here in front of my computer.”\u003c/p>\n\u003cp>[ad fullwidth]\u003c/p>\n\u003cp>For years, schools nationwide have \u003ca href=\"https://www.npr.org/2024/05/15/1247795768/children-disabilities-special-education-teacher-shortage\" target=\"_blank\" rel=\"noopener\">struggled with hiring and retaining\u003c/a> special educators. In the 2024-25 school year, \u003ca href=\"https://learningpolicyinstitute.org/product/teacher-shortages-subjects-across-states-factsheet\" target=\"_blank\" rel=\"noopener\">45 states reported\u003c/a> special education teacher shortages, and staff turnover is worse in schools that largely serve low-income students, like Riverview.\u003c/p>\n\u003cp>Some special educators say part of what makes them feel overworked is legally required paperwork layered on top of regular teaching duties. Acebu is one of a growing number of those teachers around the nation using AI to help speed up that paperwork — including for writing individualized education programs (IEPs). Educators and families maintain these detailed documents that outline goals and services students need to meet those goals at school.\u003c/p>\n\u003cp>According to \u003ca href=\"https://cdt.org/wp-content/uploads/2025/10/2025-10-28-CDT-AI-IEP-Brief-1.pdf\" target=\"_blank\" rel=\"noopener\">a recent survey\u003c/a> by the nonpartisan Center for Democracy and Technology (CDT), 57% of special education teachers polled nationwide said they used AI to help develop individualized plans for their students in the 2024-25 school year. That’s up from 39% the previous school year.\u003c/p>\n\u003cp>Along with the survey results, the CDT warned of privacy, legal and ethical risks around using AI. Other research, however, including from the University of Virginia (UVA) and the University of Central Florida (UCF), has shown that when used appropriately, AI can help special education teachers craft IEPs of equal or higher quality than when teachers produce them alone.\u003c/p>\n\u003cp>And the time saved can benefit students, too. “The more face time a student with a disability has with a teacher, that often yields better outcomes for them, both educationally, functionally — just across the board,” says Olivia Coleman, a researcher and professor at UCF who has been studying the role of AI in special education.\u003c/p>\n\u003cp>Acebu says that rings true in her classroom. She points out King, one of her eighth graders, as an example. “He was a non-reader, beginning of seventh grade. He’s reading now.” That, for Acebu, is the \u003cem>point\u003c/em> of IEPs — to put what’s on paper into practice for her students. She says that is only possible with intentional, hands-on work in the classroom.\u003c/p>\n\u003ch2>\u003cstrong>What IEPs are and why they matter\u003c/strong>\u003c/h2>\n\u003cp>Every seventh and eighth grader in Mary Acebu’s class learns differently — some work independently, some in pairs, others with headphones on and yet others with speech-to-text technology. Those differences are captured in each child’s IEP, a document required by federal law for each of the over 8 million students with disabilities in this country.\u003c/p>\n\u003cfigure class=\"wp-block-image size-large\">\u003cimg decoding=\"async\" src=\"https://npr.brightspotcdn.com/dims3/default/strip/false/crop/4000x2668+0+0/resize/1200/quality/75/format/jpeg/?url=http%3A%2F%2Fnpr-brightspot.s3.amazonaws.com%2F8d%2F57%2F8601fa57482dbd294f690c16221d%2F36a0496-tif.jpg\" alt=\"Mary Acebu has been a special education teacher for a decade at Riverview Middle School. She is part of a task force that is working on an AI policy for her school district.\">\u003cfigcaption>Mary Acebu has been a special education teacher for a decade at Riverview Middle School. She is part of a task force that is working on an AI policy for her school district.\u003cbr>\n\u003ccite> (Talia Herman for NPR)\u003c/cite>\u003c/figcaption>\u003c/figure>\n\u003cp>Every IEP includes annual goals tailored to each student’s present needs, but importantly, “also where you want them to go within the next year,” says Danielle Waterfield, Coleman’s research partner at UVA.\u003c/p>\n\u003cp>Both Coleman and Waterfield say while many teachers report feeling bogged down by the work that goes into developing IEPs, teachers also recognize they are a necessary tool for students with disabilities to get a quality education.\u003c/p>\n\u003cp>Acebu says that to develop those goals, teachers must know each student’s learning style intimately. “The key term is ‘individualized.’ No two kids are the same,” she says. For special educators, the process involves hours of meetings and a deep knowledge of complex education law and policy.\u003c/p>\n\u003cp>It used to take Acebu around 45 minutes to develop three or four IEP goals per student. She points to a big, blue binder at least 5 inches thick on her bookshelf that contains California’s education standards. “It used to be flipping through all those pages,” to find the right standard to match unique student goals, she says.\u003c/p>\n\u003cp>Then came AI.\u003c/p>\n\u003ch2>\u003cstrong>Using AI — with a ‘human touch’ \u003c/strong>\u003c/h2>\n\u003cp>A couple of years ago, Acebu began taking courses on how to safely and effectively use AI. Around the same time, her district, Mt. Diablo Unified, entered agreements with companies that offer education-focused AI tools including MagicSchool AI and Google. They promise to protect sensitive student data, a primary concern for those who warn against the risks of using AI in schools. A growing number of districts are adopting such products, though \u003ca href=\"https://www.edweek.org/technology/which-states-require-schools-to-have-ai-policies/2025/09\" target=\"_blank\" rel=\"noopener\">only a few states\u003c/a> have official AI education policies.\u003c/p>\n\u003cp>Recently, using a district-vetted tool, Acebu customized chatbots for her school and trained them on state standards, assessments and other special education data. She now uses her “little assistants” for a wide range of tasks, from creating personalized worksheets to developing IEP goals.\u003c/p>\n\u003cp>And then, she says, “you’re double-checking everything. Like you have to put that human touch, that’s the final step.”\u003c/p>\n\u003cfigure class=\"wp-block-image size-large\">\u003cimg decoding=\"async\" src=\"https://npr.brightspotcdn.com/dims3/default/strip/false/crop/4000x2668+0+0/resize/1200/quality/75/format/jpeg/?url=http%3A%2F%2Fnpr-brightspot.s3.amazonaws.com%2F5c%2F9b%2F7917c6324542801ed762bdb30d77%2F36a9904-tif.jpg\" alt=\"King, an eighth grader, went from not being able to read to reading confidently since he joined Acebu's class last year. She says that has been possible, in part, because AI has given her more time to work directly with students in the classroom and less on paperwork.\">\u003cfigcaption>King, an eighth grader, went from not being able to read to reading confidently since he joined Acebu’s class last year. She says that has been possible, in part, because AI has given her more time to work directly with students in the classroom and less on paperwork.\u003cbr>\n\u003ccite> (Talia Herman for NPR)\u003c/cite>\u003c/figcaption>\u003c/figure>\n\u003cfigure class=\"wp-block-image size-large\">\u003cimg decoding=\"async\" src=\"https://npr.brightspotcdn.com/dims3/default/strip/false/crop/4500x3000+0+0/resize/1200/quality/75/format/jpeg/?url=http%3A%2F%2Fnpr-brightspot.s3.amazonaws.com%2F46%2F00%2F54e6a456459fa6fb438ca613f8a0%2F36a9793-tif.jpg\" alt=\"For a science project, King made turtle pieces from clay. They are part of a board game he created with Acebu's help called Turtle Catastrophe. It was one of two projects from his school that was accepted at a local science fair.\">\u003cfigcaption>For a science project, King made turtle pieces from clay. They are part of a board game he created with Acebu’s help called Turtle Catastrophe. It was one of two projects from his school that was accepted at a local science fair.\u003cbr>\n\u003ccite> (Talia Herman for NPR)\u003c/cite>\u003c/figcaption>\u003c/figure>\n\u003cp>\u003ca href=\"https://journals.sagepub.com/doi/10.1177/01626434261419099\" target=\"_blank\" rel=\"noopener\">In their research,\u003c/a> Coleman and Waterfield found special education teachers nationwide are using AI to help write IEP goals, track student progress, synthesize data and create differentiated learning materials, among other things.\u003c/p>\n\u003cp>Acebu is uniquely equipped to use tech-tools: She just earned her doctorate in instructional technology and is on her district’s AI task force, which is developing an official AI policy.\u003c/p>\n\u003cp>Some of Acebu’s less tech-savvy colleagues, however, were skeptical, including Paul Stone, who has been a special educator at Riverview for 22 years.\u003c/p>\n\u003cp>Then the number of students he serves shot up.\u003c/p>\n\u003cp>“I don’t want to say it’s killing me, but it has put a huge stressor on my mental health and my life,” Stone says of his work this year. “It would be kind of nice if there were two jobs, like one paperwork job and one working with the kids.”\u003c/p>\n\u003cp>So, a few weeks ago, after a tutorial from Acebu, he gave her chatbot a shot. He was surprised by the results.\u003c/p>\n\u003cp>“It’s an amazing time-saver so far,” he says. Stone has used AI for a number of things including producing simple summaries of complicated data to present to parents at IEP meetings. “I mean, it’s not like ‘that’s it, I’m done.’ I still have to go through and check it all.”\u003c/p>\n\u003cp>He and Acebu both say it could help them, and other educators, avoid burnout. Yet, Ariana Aboulafia, who was the lead author of CDT’s report, calls AI tools “a Band-Aid” for special education teachers who feel overworked.\u003c/p>\n\u003ch2>\u003cstrong>Using AI in special education — with guardrails\u003c/strong>\u003c/h2>\n\u003cp>Band-Aid or not, more teachers \u003cem>are \u003c/em>using AI around the country. There are a litany of concerns about its use, especially in special education, which is highly regulated. “Student privacy is number one,” says Acebu. “Don’t put information there that’s gonna identify your students.” CDT’s Aboulafia adds that while the risks around privacy may be reduced if a school is using a vetted vendor, data breaches could still make that information vulnerable.\u003c/p>\n\u003cp>But not all teachers are using district-approved tools. Coleman, Waterfield and CDT’s research all found that educators around the country are using AI both formally and informally — from free consumer platforms like ChatGPT and Claude to district-approved tools like MagicSchool AI, Google Gemini and Playground IEP, among others. To help teachers navigate this complicated landscape, Waterfield and Coleman \u003ca href=\"https://ciddl.org/navigating-ai-in-iep-development-a-framework-for-ethical-practice/\" target=\"_blank\" rel=\"noopener\">developed a “decision tree”\u003c/a> for ethical AI use.\u003c/p>\n\u003cp>Another consideration is the fact that AI models can be biased, including against people with disabilities, says Aboulafia, who leads the Disability Rights in Technology Policy Project at CDT. In addition, she worries AI models built on pattern recognition are, “to a certain extent, inherently incompatible with a process that legally requires individualization.”\u003c/p>\n\u003cp>Aboulafia is most concerned about the 15% of teachers CDT’s survey found have been relying entirely on AI to develop IEPs. There must always be a “human in the loop,” she says.\u003c/p>\n\u003cp>Acebu, who happens to be her district’s teacher of the year, says these days, she comes to class just 30 minutes before her students, and leaves just after the last bell. This has improved her work-life balance and the quality of her teaching.\u003c/p>\n\u003cp>King, the eighth grader in her class who has evolved into a confident reader, also goes to math class now without any additional support.\u003c/p>\n\u003cp>“That’s the dream of every special educator,” she says, beaming. “But guess what? That takes a lot of hard work.”\u003c/p>\n\u003cp>AI tools, Acebu says, have given her more time for that kind of hard work.\u003c/p>\n\u003cp>[ad floatright]\u003c/p>\n\u003cp>\u003cem>Edited by: Nirvi Shah\u003c/em>\u003cbr>\n\u003cem>Visual design and development by: \u003c/em>\u003ca href=\"https://www.npr.org/people/348775569/la-johnson\" target=\"_blank\" rel=\"noopener\">\u003cem>LA Johnson\u003c/em>\u003c/a>\u003c/p>\n\n",
"blocks": [],
"excerpt": "A fast-growing number of special educators nationwide are using AI to create customized education plans. Despite the risks, some research shows it could improve the quality of teachers' work.",
"status": "publish",
"parent": 0,
"modified": 1779470701,
"stats": {
"hasAudio": false,
"hasVideo": false,
"hasChartOrMap": false,
"iframeSrcs": [],
"hasGoogleForm": false,
"hasGallery": false,
"hasHearkenModule": false,
"hasPolis": false,
"paragraphCount": 39,
"wordCount": 1785
},
"headData": {
"title": "Overworked and Understaffed: Special Ed Teachers Turn to AI for Help | KQED",
"description": "A fast-growing number of special educators nationwide are using AI to create customized education plans. Despite the risks, some research shows it could improve the quality of teachers' work.",
"ogTitle": "",
"ogDescription": "",
"ogImgId": "",
"twTitle": "",
"twDescription": "",
"twImgId": "",
"schema": {
"@context": "https://schema.org",
"@type": "Article",
"headline": "Overworked and Understaffed: Special Ed Teachers Turn to AI for Help",
"datePublished": "2026-05-20T10:00:51-07:00",
"dateModified": "2026-05-22T10:25:01-07:00",
"image": "https://cdn.kqed.org/wp-content/uploads/2020/02/KQED-OG-Image@1x.png"
}
},
"primaryCategory": {
"termId": 193,
"slug": "teaching-strategies",
"name": "Teaching Strategies"
},
"sticky": false,
"nprByline": "Jonaki Mehta",
"nprStoryId": "nx-s1-5810192",
"nprHtmlLink": "https://www.npr.org/2026/05/20/nx-s1-5810192/special-education-teachers-ai-ieps",
"nprRetrievedStory": "1",
"nprPubDate": "2026-05-20T05:00:00-04:00",
"nprStoryDate": "2026-05-20T05:00:00-04:00",
"nprLastModifiedDate": "2026-05-20T20:35:47.939-04:00",
"nprAudio": "https://ondemand.npr.org/anon.npr-mp3/npr/atc/2026/05/20260520_atc_mehta-ai_in_special_ed.mp3?t=progseg&e=nx-s1-5811036&p=2&seg=8&d=444&size=7115382",
"templateType": "standard",
"featuredImageType": "standard",
"excludeFromSiteSearch": "Include",
"showOnAuthorArchivePages": "No",
"articleAge": "0",
"path": "/mindshift/66358/overworked-and-understaffed-special-ed-teachers-turn-to-ai-for-help",
"audioUrl": "https://ondemand.npr.org/anon.npr-mp3/npr/atc/2026/05/20260520_atc_mehta-ai_in_special_ed.mp3?t=progseg&e=nx-s1-5811036&p=2&seg=8&d=444&size=7115382",
"audioTrackLength": null,
"parsedContent": [
{
"type": "contentString",
"content": "\u003cdiv class=\"post-body\">\u003cp>\u003cp>\u003cem>Editor’s note: NPR uses only the first names of minors in this story because it discusses their learning disabilities and placement in special education.\u003c/em>\u003c/p>\n\u003cp>BAY POINT, Calif. — The sun would just be rising when teacher Mary Acebu began her days. She’d blast music on the way to work to get energized and get to her classroom by 6:30 to prepare for her students’ arrival at 8. Often, it’d be dark by the time she headed home, sometimes with paperwork in tow.\u003c/p>\n\u003cp>Like so many special education teachers around the country, this was Acebu’s life for much of the 10 years she’s been teaching at Riverview Middle School, in this small, unincorporated northern California town.\u003c/p>\n\u003cp>“I don’t do that anymore,” she says with a laugh.\u003c/p>\n\u003cp>That’s because Acebu has been experimenting with artificial intelligence for the last two years to get through paperwork more quickly and says it’s helped her instead use precious time for student interaction. “I have time to talk to the kiddos and really build those relationships,” she says, “instead of sitting here in front of my computer.”\u003c/p>\n\u003cp>\u003c/p>\u003c/div>",
"attributes": {
"named": {},
"numeric": []
}
},
{
"type": "component",
"content": "",
"name": "ad",
"attributes": {
"named": {
"label": "fullwidth"
},
"numeric": [
"fullwidth"
]
}
},
{
"type": "contentString",
"content": "\u003cdiv class=\"post-body\">\u003cp>\u003c/p>\n\u003cp>For years, schools nationwide have \u003ca href=\"https://www.npr.org/2024/05/15/1247795768/children-disabilities-special-education-teacher-shortage\" target=\"_blank\" rel=\"noopener\">struggled with hiring and retaining\u003c/a> special educators. In the 2024-25 school year, \u003ca href=\"https://learningpolicyinstitute.org/product/teacher-shortages-subjects-across-states-factsheet\" target=\"_blank\" rel=\"noopener\">45 states reported\u003c/a> special education teacher shortages, and staff turnover is worse in schools that largely serve low-income students, like Riverview.\u003c/p>\n\u003cp>Some special educators say part of what makes them feel overworked is legally required paperwork layered on top of regular teaching duties. Acebu is one of a growing number of those teachers around the nation using AI to help speed up that paperwork — including for writing individualized education programs (IEPs). Educators and families maintain these detailed documents that outline goals and services students need to meet those goals at school.\u003c/p>\n\u003cp>According to \u003ca href=\"https://cdt.org/wp-content/uploads/2025/10/2025-10-28-CDT-AI-IEP-Brief-1.pdf\" target=\"_blank\" rel=\"noopener\">a recent survey\u003c/a> by the nonpartisan Center for Democracy and Technology (CDT), 57% of special education teachers polled nationwide said they used AI to help develop individualized plans for their students in the 2024-25 school year. That’s up from 39% the previous school year.\u003c/p>\n\u003cp>Along with the survey results, the CDT warned of privacy, legal and ethical risks around using AI. Other research, however, including from the University of Virginia (UVA) and the University of Central Florida (UCF), has shown that when used appropriately, AI can help special education teachers craft IEPs of equal or higher quality than when teachers produce them alone.\u003c/p>\n\u003cp>And the time saved can benefit students, too. “The more face time a student with a disability has with a teacher, that often yields better outcomes for them, both educationally, functionally — just across the board,” says Olivia Coleman, a researcher and professor at UCF who has been studying the role of AI in special education.\u003c/p>\n\u003cp>Acebu says that rings true in her classroom. She points out King, one of her eighth graders, as an example. “He was a non-reader, beginning of seventh grade. He’s reading now.” That, for Acebu, is the \u003cem>point\u003c/em> of IEPs — to put what’s on paper into practice for her students. She says that is only possible with intentional, hands-on work in the classroom.\u003c/p>\n\u003ch2>\u003cstrong>What IEPs are and why they matter\u003c/strong>\u003c/h2>\n\u003cp>Every seventh and eighth grader in Mary Acebu’s class learns differently — some work independently, some in pairs, others with headphones on and yet others with speech-to-text technology. Those differences are captured in each child’s IEP, a document required by federal law for each of the over 8 million students with disabilities in this country.\u003c/p>\n\u003cfigure class=\"wp-block-image size-large\">\u003cimg decoding=\"async\" src=\"https://npr.brightspotcdn.com/dims3/default/strip/false/crop/4000x2668+0+0/resize/1200/quality/75/format/jpeg/?url=http%3A%2F%2Fnpr-brightspot.s3.amazonaws.com%2F8d%2F57%2F8601fa57482dbd294f690c16221d%2F36a0496-tif.jpg\" alt=\"Mary Acebu has been a special education teacher for a decade at Riverview Middle School. She is part of a task force that is working on an AI policy for her school district.\">\u003cfigcaption>Mary Acebu has been a special education teacher for a decade at Riverview Middle School. She is part of a task force that is working on an AI policy for her school district.\u003cbr>\n\u003ccite> (Talia Herman for NPR)\u003c/cite>\u003c/figcaption>\u003c/figure>\n\u003cp>Every IEP includes annual goals tailored to each student’s present needs, but importantly, “also where you want them to go within the next year,” says Danielle Waterfield, Coleman’s research partner at UVA.\u003c/p>\n\u003cp>Both Coleman and Waterfield say while many teachers report feeling bogged down by the work that goes into developing IEPs, teachers also recognize they are a necessary tool for students with disabilities to get a quality education.\u003c/p>\n\u003cp>Acebu says that to develop those goals, teachers must know each student’s learning style intimately. “The key term is ‘individualized.’ No two kids are the same,” she says. For special educators, the process involves hours of meetings and a deep knowledge of complex education law and policy.\u003c/p>\n\u003cp>It used to take Acebu around 45 minutes to develop three or four IEP goals per student. She points to a big, blue binder at least 5 inches thick on her bookshelf that contains California’s education standards. “It used to be flipping through all those pages,” to find the right standard to match unique student goals, she says.\u003c/p>\n\u003cp>Then came AI.\u003c/p>\n\u003ch2>\u003cstrong>Using AI — with a ‘human touch’ \u003c/strong>\u003c/h2>\n\u003cp>A couple of years ago, Acebu began taking courses on how to safely and effectively use AI. Around the same time, her district, Mt. Diablo Unified, entered agreements with companies that offer education-focused AI tools including MagicSchool AI and Google. They promise to protect sensitive student data, a primary concern for those who warn against the risks of using AI in schools. A growing number of districts are adopting such products, though \u003ca href=\"https://www.edweek.org/technology/which-states-require-schools-to-have-ai-policies/2025/09\" target=\"_blank\" rel=\"noopener\">only a few states\u003c/a> have official AI education policies.\u003c/p>\n\u003cp>Recently, using a district-vetted tool, Acebu customized chatbots for her school and trained them on state standards, assessments and other special education data. She now uses her “little assistants” for a wide range of tasks, from creating personalized worksheets to developing IEP goals.\u003c/p>\n\u003cp>And then, she says, “you’re double-checking everything. Like you have to put that human touch, that’s the final step.”\u003c/p>\n\u003cfigure class=\"wp-block-image size-large\">\u003cimg decoding=\"async\" src=\"https://npr.brightspotcdn.com/dims3/default/strip/false/crop/4000x2668+0+0/resize/1200/quality/75/format/jpeg/?url=http%3A%2F%2Fnpr-brightspot.s3.amazonaws.com%2F5c%2F9b%2F7917c6324542801ed762bdb30d77%2F36a9904-tif.jpg\" alt=\"King, an eighth grader, went from not being able to read to reading confidently since he joined Acebu's class last year. She says that has been possible, in part, because AI has given her more time to work directly with students in the classroom and less on paperwork.\">\u003cfigcaption>King, an eighth grader, went from not being able to read to reading confidently since he joined Acebu’s class last year. She says that has been possible, in part, because AI has given her more time to work directly with students in the classroom and less on paperwork.\u003cbr>\n\u003ccite> (Talia Herman for NPR)\u003c/cite>\u003c/figcaption>\u003c/figure>\n\u003cfigure class=\"wp-block-image size-large\">\u003cimg decoding=\"async\" src=\"https://npr.brightspotcdn.com/dims3/default/strip/false/crop/4500x3000+0+0/resize/1200/quality/75/format/jpeg/?url=http%3A%2F%2Fnpr-brightspot.s3.amazonaws.com%2F46%2F00%2F54e6a456459fa6fb438ca613f8a0%2F36a9793-tif.jpg\" alt=\"For a science project, King made turtle pieces from clay. They are part of a board game he created with Acebu's help called Turtle Catastrophe. It was one of two projects from his school that was accepted at a local science fair.\">\u003cfigcaption>For a science project, King made turtle pieces from clay. They are part of a board game he created with Acebu’s help called Turtle Catastrophe. It was one of two projects from his school that was accepted at a local science fair.\u003cbr>\n\u003ccite> (Talia Herman for NPR)\u003c/cite>\u003c/figcaption>\u003c/figure>\n\u003cp>\u003ca href=\"https://journals.sagepub.com/doi/10.1177/01626434261419099\" target=\"_blank\" rel=\"noopener\">In their research,\u003c/a> Coleman and Waterfield found special education teachers nationwide are using AI to help write IEP goals, track student progress, synthesize data and create differentiated learning materials, among other things.\u003c/p>\n\u003cp>Acebu is uniquely equipped to use tech-tools: She just earned her doctorate in instructional technology and is on her district’s AI task force, which is developing an official AI policy.\u003c/p>\n\u003cp>Some of Acebu’s less tech-savvy colleagues, however, were skeptical, including Paul Stone, who has been a special educator at Riverview for 22 years.\u003c/p>\n\u003cp>Then the number of students he serves shot up.\u003c/p>\n\u003cp>“I don’t want to say it’s killing me, but it has put a huge stressor on my mental health and my life,” Stone says of his work this year. “It would be kind of nice if there were two jobs, like one paperwork job and one working with the kids.”\u003c/p>\n\u003cp>So, a few weeks ago, after a tutorial from Acebu, he gave her chatbot a shot. He was surprised by the results.\u003c/p>\n\u003cp>“It’s an amazing time-saver so far,” he says. Stone has used AI for a number of things including producing simple summaries of complicated data to present to parents at IEP meetings. “I mean, it’s not like ‘that’s it, I’m done.’ I still have to go through and check it all.”\u003c/p>\n\u003cp>He and Acebu both say it could help them, and other educators, avoid burnout. Yet, Ariana Aboulafia, who was the lead author of CDT’s report, calls AI tools “a Band-Aid” for special education teachers who feel overworked.\u003c/p>\n\u003ch2>\u003cstrong>Using AI in special education — with guardrails\u003c/strong>\u003c/h2>\n\u003cp>Band-Aid or not, more teachers \u003cem>are \u003c/em>using AI around the country. There are a litany of concerns about its use, especially in special education, which is highly regulated. “Student privacy is number one,” says Acebu. “Don’t put information there that’s gonna identify your students.” CDT’s Aboulafia adds that while the risks around privacy may be reduced if a school is using a vetted vendor, data breaches could still make that information vulnerable.\u003c/p>\n\u003cp>But not all teachers are using district-approved tools. Coleman, Waterfield and CDT’s research all found that educators around the country are using AI both formally and informally — from free consumer platforms like ChatGPT and Claude to district-approved tools like MagicSchool AI, Google Gemini and Playground IEP, among others. To help teachers navigate this complicated landscape, Waterfield and Coleman \u003ca href=\"https://ciddl.org/navigating-ai-in-iep-development-a-framework-for-ethical-practice/\" target=\"_blank\" rel=\"noopener\">developed a “decision tree”\u003c/a> for ethical AI use.\u003c/p>\n\u003cp>Another consideration is the fact that AI models can be biased, including against people with disabilities, says Aboulafia, who leads the Disability Rights in Technology Policy Project at CDT. In addition, she worries AI models built on pattern recognition are, “to a certain extent, inherently incompatible with a process that legally requires individualization.”\u003c/p>\n\u003cp>Aboulafia is most concerned about the 15% of teachers CDT’s survey found have been relying entirely on AI to develop IEPs. There must always be a “human in the loop,” she says.\u003c/p>\n\u003cp>Acebu, who happens to be her district’s teacher of the year, says these days, she comes to class just 30 minutes before her students, and leaves just after the last bell. This has improved her work-life balance and the quality of her teaching.\u003c/p>\n\u003cp>King, the eighth grader in her class who has evolved into a confident reader, also goes to math class now without any additional support.\u003c/p>\n\u003cp>“That’s the dream of every special educator,” she says, beaming. “But guess what? That takes a lot of hard work.”\u003c/p>\n\u003cp>AI tools, Acebu says, have given her more time for that kind of hard work.\u003c/p>\n\u003cp>\u003c/p>\u003c/div>",
"attributes": {
"named": {},
"numeric": []
}
},
{
"type": "component",
"content": "",
"name": "ad",
"attributes": {
"named": {
"label": "floatright"
},
"numeric": [
"floatright"
]
}
},
{
"type": "contentString",
"content": "\u003cdiv class=\"post-body\">\u003cp>\u003c/p>\n\u003cp>\u003cem>Edited by: Nirvi Shah\u003c/em>\u003cbr>\n\u003cem>Visual design and development by: \u003c/em>\u003ca href=\"https://www.npr.org/people/348775569/la-johnson\" target=\"_blank\" rel=\"noopener\">\u003cem>LA Johnson\u003c/em>\u003c/a>\u003c/p>\n\n\u003c/div>\u003c/p>",
"attributes": {
"named": {},
"numeric": []
}
}
],
"link": "/mindshift/66358/overworked-and-understaffed-special-ed-teachers-turn-to-ai-for-help",
"authors": [
"byline_mindshift_66358"
],
"programs": [
"mindshift_21847"
],
"categories": [
"mindshift_193"
],
"tags": [
"mindshift_1023",
"mindshift_20934"
],
"featImg": "mindshift_66359",
"label": "mindshift_21847"
},
"mindshift_66311": {
"type": "posts",
"id": "mindshift_66311",
"meta": {
"index": "posts_1716263798",
"site": "mindshift",
"id": "66311",
"score": null,
"sort": [
1777396146000
]
},
"guestAuthors": [],
"slug": "should-schools-get-rid-of-homework-the-answer-is-complex-and-ai-contributes",
"title": "Should Schools Get Rid of Homework? The Answer is Complex and AI Contributes",
"publishDate": 1777396146,
"format": "standard",
"headTitle": "Should Schools Get Rid of Homework? The Answer is Complex and AI Contributes | KQED",
"labelTerm": {
"site": "mindshift"
},
"content": "\u003cp>A few days into the new semester this January, the LaSalle Parish school district in rural Louisiana made a pronouncement: No more homework.\u003c/p>\n\u003cp>Since then, none of the 2,500 students in this district — from the youngest learners up through high school seniors — have been required to do schoolwork at home. Parents can request practice problems if they’d like, Superintendent Jonathan Garrett said, but that work won’t be mandatory or graded.\u003c/p>\n\u003cp>Homework assignments, it turned out, were among the biggest sources of complaints Garrett had heard from parents and students over the years.\u003c/p>\n\u003cp>“When there was a negative feeling about school, it usually stemmed from what kids are bringing home, the frustrations they feel completing that, and that parents and guardians feel trying to help them complete it,” he said in an interview.\u003c/p>\n\u003cp>Beyond that, Garrett said the move was driven by concerns – shared by many educators – that much of the homework students are assigned – especially in math – is needlessly repetitive, takes too long to complete and hasn’t adapted to the challenges posed by Artificial Intelligence.\u003c/p>\n\u003cp>[ad fullwidth]\u003c/p>\n\u003cp>The response to Garrett’s announcement was swift — and overwhelmingly positive. The message is the district’s most “liked”\u003ca href=\"https://www.facebook.com/photo?fbid=1444965060964889&set=a.499624705498934\" target=\"_blank\" rel=\"noopener\"> post on Facebook\u003c/a> by far this year, with hundreds of shares — many of them by parents from neighboring parishes asking how they could get their own schools on board.\u003c/p>\n\u003cp>The scope of the district’s no-homework guidance is new, but it follows a trend that educators and researchers have been noticing for years: More teachers are moving away from homework.\u003c/p>\n\u003cp>Federal survey data shows that the amount of math homework assigned to fourth and eighth grade students, in particular, has been steadily declining for the past decade.\u003c/p>\n\u003cp>Some educators and parents say this is a good thing — students shouldn’t spend six or more hours a day at school and still have additional schoolwork to complete at home. But the research on homework is complicated.\u003c/p>\n\u003cp>Some studies show that students who spend more time on homework\u003ca href=\"https://pmc.ncbi.nlm.nih.gov/articles/PMC8025066/\" target=\"_blank\" rel=\"noopener\"> perform better than their peers\u003c/a>. For example, a longitudinal study released in 2021 of more than 6,000 students in Germany, Uruguay and the Netherlands found that lower-performing students who increased the amount of time they spent on math homework performed better in math, even one year later.\u003c/p>\n\u003cp>Other studies, however, suggest homework has minimal outcomes on academic performance: A 1998 study of more than 700 U.S. students led by a researcher at Duke University found that more homework assigned in elementary grades had no significant effect on standardized test scores. The researchers did find small positive gains on class grades when they looked at both test scores and the proportion of homework students completed.\u003c/p>\n\u003cp>More homework was also associated with negative attitudes about school for younger children in the study.\u003c/p>\n\u003cp>“The best educators figured out a long time ago that we can control what we can control,” and that’s what happens during the school day, Superintendent Garrett said, not homework. “There has been a shift away from it naturally anyway, and I felt like this made it equitable across our entire school system.”\u003c/p>\n\u003ch2>In math especially, students need practice\u003c/h2>\n\u003cp>The debate over homework has swung back and forth for more than a century, and the tide of public opinion has shifted every few years. It’s likely to continue changing for a simple reason: Researching homework is a challenge.\u003c/p>\n\u003cp>There’s no good way to isolate the amount of time spent on homework and its effects on students, because it may take one student five minutes to complete the same math problem that another student spent 45 minutes on. That extra time doesn’t necessarily result in the struggling student performing better than the student who grasped the assignment more quickly.\u003c/p>\n\u003cp>However, just like playing the violin or hitting a baseball, or any other skill that requires training, there is evidence that students need practice to master academic subjects, particularly in math.\u003c/p>\n\u003cp>Some experts worry the overall decrease in homework could be a problem for math achievement, at a time when\u003ca href=\"https://hechingerreport.org/naep-test-2024-dismal-report/\" target=\"_blank\" rel=\"noopener\"> math scores across the country are already at a dismal low\u003c/a>.\u003c/p>\n\u003cp>“The best argument for homework is that mathematical procedures require practice, and you don’t want to waste classroom time on practice, so you send that home,” said Tom Loveless, a researcher and former teacher who has studied homework.\u003c/p>\n\u003ch2>\u003cstrong>The effects of AI on homework\u003c/strong>\u003c/h2>\n\u003cp>Generative artificial intelligence has added a new wrinkle to the homework debate, too. More than half of teens said they used chatbots to help with schoolwork, and\u003ca href=\"https://www.pewresearch.org/internet/2026/02/24/how-teens-use-and-view-ai/\" target=\"_blank\" rel=\"noopener\"> 1 in 10 said they used virtual assistants\u003c/a> to do all or most of their schoolwork, according to a recent survey by Pew Research Center.\u003c/p>\n\u003cp>A different survey of teachers by the EdWeek Research Center found that 40 percent said homework assignments had decreased over the past two years, and of those, 29 percent said it was\u003ca href=\"https://www.edweek.org/leadership/are-schools-assigning-less-homework-a-new-survey-offers-answers/2026/02\" target=\"_blank\" rel=\"noopener\"> because students’ use of AI had lessened the value of homework\u003c/a>.\u003c/p>\n\u003cp>Between 1996 and 2015, very few fourth graders — between 4 and 6 percent — reported being given no math homework the previous night, according to surveys from the Nation’s Report Card. By 2024, that percentage was up to more than a quarter. There was a similar trend for eighth graders.\u003c/p>\n\u003cp>Ariel Taylor Smith, senior director of the Center for Policy and Action at the National Parents Union, a nonprofit that advocates for parents, has seen this trend in her own fourth grader’s public elementary school class in Vermont, whose teacher doesn’t assign homework.\u003c/p>\n\u003cp>“The thing they point to is that it’s an equity issue, and not all parents have the same availability and ability to support their students,” said Smith.\u003c/p>\n\u003cp>She believes, however, that students should do some homework without the help of their parents. “I would make the argument that if a kid is really far behind in school, that’s an equity issue. They need the additional time to practice.”\u003c/p>\n\u003cp>Smith said she and her mother create their own homework now for her son: reading exercises and flash cards in math. Kids, she said, “need more practice. … Sometimes, you do have to practice the boring stuff, like math.”\u003c/p>\n\u003cp>Not everyone feels this way about homework. For Jim Malliard’s two children in Franklin, Pa., adverse experiences at school became a barrier to completing homework.\u003c/p>\n\u003cp>“It became a fight because the kids had so much school-based anxiety from trauma and bullying at school that they didn’t want to deal with school when they got home,” said Malliard, whose kids attended a public high school.\u003c/p>\n\u003cp>Malliard, who\u003ca href=\"https://candyappleadvocacy.com\" target=\"_blank\" rel=\"noopener\"> writes\u003c/a> about education issues and is a full-time caregiver to his wife, doesn’t think his children were overburdened with homework at their school, but he also doesn’t believe they were benefiting from it.\u003c/p>\n\u003cp>“The teachers would tell us homework only takes 15 minutes a night — sure, if a kid sits there and does it right away and is attentive and wants to do it,” Malliard said. “It was getting to be an hour for us.”\u003c/p>\n\u003cp>He eventually enrolled his children in a virtual charter school, which they attended for the rest of their K-12 schooling.\u003c/p>\n\u003ch2>\u003cstrong>How much is enough?\u003c/strong>\u003c/h2>\n\u003cp>Over the years, research has attempted to answer the thorny question of how much homework is appropriate, with varying degrees of success.\u003c/p>\n\u003cp>Education groups and researchers generally recommend 10 minutes of homework each night per grade level. But it’s almost impossible to assign work that will take every student the same amount of time to complete, and research has shown there are harmful effects from too much time spent on homework.\u003c/p>\n\u003cp>A survey published in 2014 out of Stanford University that looked at more than 4,300 students in high-performing California high school schools found that the benefit of homework for high school students\u003ca href=\"https://ed.stanford.edu/news/more-two-hours-homework-may-be-counterproductive-research-suggests\" target=\"_blank\" rel=\"noopener\"> plateaus after two hours a night\u003c/a>. Beyond that, the researchers found, it can lead to more stress and poor sleep.\u003c/p>\n\u003cp>Research on homework tends to focus on the amount of time students spend on it rather than the quality or purpose of the assignments, said Joyce Epstein, who has studied homework and is the co-director of the Center on School, Family, and Community Partnerships at the Johns Hopkins University School of Education.\u003c/p>\n\u003cp>One option worth considering, Epstein said, is to design homework that has a specific purpose but is perhaps shorter than traditional homework assignments. Giving students the opportunity to practice is important, she said, particularly in math, where concepts build on each other and move relentlessly forward throughout the year.\u003c/p>\n\u003cp>“The interesting issue for folks to consider is not should there be more homework, but should there be better homework,” Epstein said. “Better homework in math might be knowing the fact that kids don’t have to be practicing for hours, 10 to 20 examples,” when they could establish mastery in less time.\u003c/p>\n\u003cp>When students are completing math homework on their own but doing the problems incorrectly, some educators say it takes longer to reteach them the right way in class the next day.\u003c/p>\n\u003cp>Wendy Birhanzel, superintendent of Harrison School District 2 in Colorado, said her district has taken the approach recommended by Epstein, of focusing on the quality of homework while assigning less of it.\u003c/p>\n\u003cp>Rather than long “drill and kill” worksheets she remembers from her time as a student, Birhanzel said elementary students in the district might have a reading assignment, a few math problems and a small writing sample. “It’s more purposeful and less intensive,” Birhanzel said.\u003c/p>\n\u003cp>In Louisiana’s LaSalle Parish, Superintendent Garrett said that to account for the lost practice time, he has given math teachers permission to slow down their instruction and give students time in class to practice concepts, even if that means they don’t cover as much content during the school year.\u003c/p>\n\u003cp>“We felt like doing that would actually be more beneficial than racing through and covering every single thing that was listed. We’ll see,” he said. “This might be something that helps us in the long run.”\u003c/p>\n\u003cp>[ad floatright]\u003c/p>\n\u003cp>\u003cem>This story was produced by\u003c/em>\u003ca href=\"https://hechingerreport.org/\" target=\"_blank\" rel=\"noopener\"> The Hechinger Report\u003c/a>\u003cem>, a nonprofit, independent news organization focused on inequality and innovation in education. Contact writer Ariel Gilreath on Signal at arielgilreath.46 or at gilreath@hechingerreport.org\u003c/em>.\u003c/p>\n\n",
"blocks": [],
"excerpt": "Some experts worry that less homework could be a problem for math achievement, at a time when test scores nationwide are already at a dismal low.",
"status": "publish",
"parent": 0,
"modified": 1777655634,
"stats": {
"hasAudio": false,
"hasVideo": false,
"hasChartOrMap": false,
"iframeSrcs": [],
"hasGoogleForm": false,
"hasGallery": false,
"hasHearkenModule": false,
"hasPolis": false,
"paragraphCount": 44,
"wordCount": 1797
},
"headData": {
"title": "Should Schools Get Rid of Homework? The Answer is Complex and AI Contributes | KQED",
"description": "Some experts worry that less homework could be a problem for math achievement, at a time when test scores nationwide are already at a dismal low.",
"ogTitle": "",
"ogDescription": "",
"ogImgId": "",
"twTitle": "",
"twDescription": "",
"twImgId": "",
"schema": {
"@context": "https://schema.org",
"@type": "Article",
"headline": "Should Schools Get Rid of Homework? The Answer is Complex and AI Contributes",
"datePublished": "2026-04-28T10:09:06-07:00",
"dateModified": "2026-05-01T10:13:54-07:00",
"image": "https://cdn.kqed.org/wp-content/uploads/2020/02/KQED-OG-Image@1x.png"
}
},
"primaryCategory": {
"termId": 21504,
"slug": "education-research",
"name": "Education research"
},
"sticky": false,
"nprByline": "Ariel Gilreath, \u003ca href=\"https://hechingerreport.org/\">The Hechinger Report\u003c/a>",
"nprStoryId": "nx-s1-5795647",
"nprHtmlLink": "https://www.npr.org/2026/04/28/nx-s1-5795647/should-schools-get-rid-of-homework",
"nprRetrievedStory": "1",
"nprPubDate": "2026-04-28T05:00:00-04:00",
"nprStoryDate": "2026-04-28T05:00:00-04:00",
"nprLastModifiedDate": "2026-04-28T05:00:25.432-04:00",
"templateType": "standard",
"featuredImageType": "standard",
"excludeFromSiteSearch": "Include",
"showOnAuthorArchivePages": "No",
"articleAge": "0",
"path": "/mindshift/66311/should-schools-get-rid-of-homework-the-answer-is-complex-and-ai-contributes",
"audioTrackLength": null,
"parsedContent": [
{
"type": "contentString",
"content": "\u003cdiv class=\"post-body\">\u003cp>\u003cp>A few days into the new semester this January, the LaSalle Parish school district in rural Louisiana made a pronouncement: No more homework.\u003c/p>\n\u003cp>Since then, none of the 2,500 students in this district — from the youngest learners up through high school seniors — have been required to do schoolwork at home. Parents can request practice problems if they’d like, Superintendent Jonathan Garrett said, but that work won’t be mandatory or graded.\u003c/p>\n\u003cp>Homework assignments, it turned out, were among the biggest sources of complaints Garrett had heard from parents and students over the years.\u003c/p>\n\u003cp>“When there was a negative feeling about school, it usually stemmed from what kids are bringing home, the frustrations they feel completing that, and that parents and guardians feel trying to help them complete it,” he said in an interview.\u003c/p>\n\u003cp>Beyond that, Garrett said the move was driven by concerns – shared by many educators – that much of the homework students are assigned – especially in math – is needlessly repetitive, takes too long to complete and hasn’t adapted to the challenges posed by Artificial Intelligence.\u003c/p>\n\u003cp>\u003c/p>\u003c/div>",
"attributes": {
"named": {},
"numeric": []
}
},
{
"type": "component",
"content": "",
"name": "ad",
"attributes": {
"named": {
"label": "fullwidth"
},
"numeric": [
"fullwidth"
]
}
},
{
"type": "contentString",
"content": "\u003cdiv class=\"post-body\">\u003cp>\u003c/p>\n\u003cp>The response to Garrett’s announcement was swift — and overwhelmingly positive. The message is the district’s most “liked”\u003ca href=\"https://www.facebook.com/photo?fbid=1444965060964889&set=a.499624705498934\" target=\"_blank\" rel=\"noopener\"> post on Facebook\u003c/a> by far this year, with hundreds of shares — many of them by parents from neighboring parishes asking how they could get their own schools on board.\u003c/p>\n\u003cp>The scope of the district’s no-homework guidance is new, but it follows a trend that educators and researchers have been noticing for years: More teachers are moving away from homework.\u003c/p>\n\u003cp>Federal survey data shows that the amount of math homework assigned to fourth and eighth grade students, in particular, has been steadily declining for the past decade.\u003c/p>\n\u003cp>Some educators and parents say this is a good thing — students shouldn’t spend six or more hours a day at school and still have additional schoolwork to complete at home. But the research on homework is complicated.\u003c/p>\n\u003cp>Some studies show that students who spend more time on homework\u003ca href=\"https://pmc.ncbi.nlm.nih.gov/articles/PMC8025066/\" target=\"_blank\" rel=\"noopener\"> perform better than their peers\u003c/a>. For example, a longitudinal study released in 2021 of more than 6,000 students in Germany, Uruguay and the Netherlands found that lower-performing students who increased the amount of time they spent on math homework performed better in math, even one year later.\u003c/p>\n\u003cp>Other studies, however, suggest homework has minimal outcomes on academic performance: A 1998 study of more than 700 U.S. students led by a researcher at Duke University found that more homework assigned in elementary grades had no significant effect on standardized test scores. The researchers did find small positive gains on class grades when they looked at both test scores and the proportion of homework students completed.\u003c/p>\n\u003cp>More homework was also associated with negative attitudes about school for younger children in the study.\u003c/p>\n\u003cp>“The best educators figured out a long time ago that we can control what we can control,” and that’s what happens during the school day, Superintendent Garrett said, not homework. “There has been a shift away from it naturally anyway, and I felt like this made it equitable across our entire school system.”\u003c/p>\n\u003ch2>In math especially, students need practice\u003c/h2>\n\u003cp>The debate over homework has swung back and forth for more than a century, and the tide of public opinion has shifted every few years. It’s likely to continue changing for a simple reason: Researching homework is a challenge.\u003c/p>\n\u003cp>There’s no good way to isolate the amount of time spent on homework and its effects on students, because it may take one student five minutes to complete the same math problem that another student spent 45 minutes on. That extra time doesn’t necessarily result in the struggling student performing better than the student who grasped the assignment more quickly.\u003c/p>\n\u003cp>However, just like playing the violin or hitting a baseball, or any other skill that requires training, there is evidence that students need practice to master academic subjects, particularly in math.\u003c/p>\n\u003cp>Some experts worry the overall decrease in homework could be a problem for math achievement, at a time when\u003ca href=\"https://hechingerreport.org/naep-test-2024-dismal-report/\" target=\"_blank\" rel=\"noopener\"> math scores across the country are already at a dismal low\u003c/a>.\u003c/p>\n\u003cp>“The best argument for homework is that mathematical procedures require practice, and you don’t want to waste classroom time on practice, so you send that home,” said Tom Loveless, a researcher and former teacher who has studied homework.\u003c/p>\n\u003ch2>\u003cstrong>The effects of AI on homework\u003c/strong>\u003c/h2>\n\u003cp>Generative artificial intelligence has added a new wrinkle to the homework debate, too. More than half of teens said they used chatbots to help with schoolwork, and\u003ca href=\"https://www.pewresearch.org/internet/2026/02/24/how-teens-use-and-view-ai/\" target=\"_blank\" rel=\"noopener\"> 1 in 10 said they used virtual assistants\u003c/a> to do all or most of their schoolwork, according to a recent survey by Pew Research Center.\u003c/p>\n\u003cp>A different survey of teachers by the EdWeek Research Center found that 40 percent said homework assignments had decreased over the past two years, and of those, 29 percent said it was\u003ca href=\"https://www.edweek.org/leadership/are-schools-assigning-less-homework-a-new-survey-offers-answers/2026/02\" target=\"_blank\" rel=\"noopener\"> because students’ use of AI had lessened the value of homework\u003c/a>.\u003c/p>\n\u003cp>Between 1996 and 2015, very few fourth graders — between 4 and 6 percent — reported being given no math homework the previous night, according to surveys from the Nation’s Report Card. By 2024, that percentage was up to more than a quarter. There was a similar trend for eighth graders.\u003c/p>\n\u003cp>Ariel Taylor Smith, senior director of the Center for Policy and Action at the National Parents Union, a nonprofit that advocates for parents, has seen this trend in her own fourth grader’s public elementary school class in Vermont, whose teacher doesn’t assign homework.\u003c/p>\n\u003cp>“The thing they point to is that it’s an equity issue, and not all parents have the same availability and ability to support their students,” said Smith.\u003c/p>\n\u003cp>She believes, however, that students should do some homework without the help of their parents. “I would make the argument that if a kid is really far behind in school, that’s an equity issue. They need the additional time to practice.”\u003c/p>\n\u003cp>Smith said she and her mother create their own homework now for her son: reading exercises and flash cards in math. Kids, she said, “need more practice. … Sometimes, you do have to practice the boring stuff, like math.”\u003c/p>\n\u003cp>Not everyone feels this way about homework. For Jim Malliard’s two children in Franklin, Pa., adverse experiences at school became a barrier to completing homework.\u003c/p>\n\u003cp>“It became a fight because the kids had so much school-based anxiety from trauma and bullying at school that they didn’t want to deal with school when they got home,” said Malliard, whose kids attended a public high school.\u003c/p>\n\u003cp>Malliard, who\u003ca href=\"https://candyappleadvocacy.com\" target=\"_blank\" rel=\"noopener\"> writes\u003c/a> about education issues and is a full-time caregiver to his wife, doesn’t think his children were overburdened with homework at their school, but he also doesn’t believe they were benefiting from it.\u003c/p>\n\u003cp>“The teachers would tell us homework only takes 15 minutes a night — sure, if a kid sits there and does it right away and is attentive and wants to do it,” Malliard said. “It was getting to be an hour for us.”\u003c/p>\n\u003cp>He eventually enrolled his children in a virtual charter school, which they attended for the rest of their K-12 schooling.\u003c/p>\n\u003ch2>\u003cstrong>How much is enough?\u003c/strong>\u003c/h2>\n\u003cp>Over the years, research has attempted to answer the thorny question of how much homework is appropriate, with varying degrees of success.\u003c/p>\n\u003cp>Education groups and researchers generally recommend 10 minutes of homework each night per grade level. But it’s almost impossible to assign work that will take every student the same amount of time to complete, and research has shown there are harmful effects from too much time spent on homework.\u003c/p>\n\u003cp>A survey published in 2014 out of Stanford University that looked at more than 4,300 students in high-performing California high school schools found that the benefit of homework for high school students\u003ca href=\"https://ed.stanford.edu/news/more-two-hours-homework-may-be-counterproductive-research-suggests\" target=\"_blank\" rel=\"noopener\"> plateaus after two hours a night\u003c/a>. Beyond that, the researchers found, it can lead to more stress and poor sleep.\u003c/p>\n\u003cp>Research on homework tends to focus on the amount of time students spend on it rather than the quality or purpose of the assignments, said Joyce Epstein, who has studied homework and is the co-director of the Center on School, Family, and Community Partnerships at the Johns Hopkins University School of Education.\u003c/p>\n\u003cp>One option worth considering, Epstein said, is to design homework that has a specific purpose but is perhaps shorter than traditional homework assignments. Giving students the opportunity to practice is important, she said, particularly in math, where concepts build on each other and move relentlessly forward throughout the year.\u003c/p>\n\u003cp>“The interesting issue for folks to consider is not should there be more homework, but should there be better homework,” Epstein said. “Better homework in math might be knowing the fact that kids don’t have to be practicing for hours, 10 to 20 examples,” when they could establish mastery in less time.\u003c/p>\n\u003cp>When students are completing math homework on their own but doing the problems incorrectly, some educators say it takes longer to reteach them the right way in class the next day.\u003c/p>\n\u003cp>Wendy Birhanzel, superintendent of Harrison School District 2 in Colorado, said her district has taken the approach recommended by Epstein, of focusing on the quality of homework while assigning less of it.\u003c/p>\n\u003cp>Rather than long “drill and kill” worksheets she remembers from her time as a student, Birhanzel said elementary students in the district might have a reading assignment, a few math problems and a small writing sample. “It’s more purposeful and less intensive,” Birhanzel said.\u003c/p>\n\u003cp>In Louisiana’s LaSalle Parish, Superintendent Garrett said that to account for the lost practice time, he has given math teachers permission to slow down their instruction and give students time in class to practice concepts, even if that means they don’t cover as much content during the school year.\u003c/p>\n\u003cp>“We felt like doing that would actually be more beneficial than racing through and covering every single thing that was listed. We’ll see,” he said. “This might be something that helps us in the long run.”\u003c/p>\n\u003cp>\u003c/p>\u003c/div>",
"attributes": {
"named": {},
"numeric": []
}
},
{
"type": "component",
"content": "",
"name": "ad",
"attributes": {
"named": {
"label": "floatright"
},
"numeric": [
"floatright"
]
}
},
{
"type": "contentString",
"content": "\u003cdiv class=\"post-body\">\u003cp>\u003c/p>\n\u003cp>\u003cem>This story was produced by\u003c/em>\u003ca href=\"https://hechingerreport.org/\" target=\"_blank\" rel=\"noopener\"> The Hechinger Report\u003c/a>\u003cem>, a nonprofit, independent news organization focused on inequality and innovation in education. Contact writer Ariel Gilreath on Signal at arielgilreath.46 or at gilreath@hechingerreport.org\u003c/em>.\u003c/p>\n\n\u003c/div>\u003c/p>",
"attributes": {
"named": {},
"numeric": []
}
}
],
"link": "/mindshift/66311/should-schools-get-rid-of-homework-the-answer-is-complex-and-ai-contributes",
"authors": [
"byline_mindshift_66311"
],
"categories": [
"mindshift_21504"
],
"tags": [
"mindshift_1023",
"mindshift_563",
"mindshift_20893",
"mindshift_20816"
],
"featImg": "mindshift_66312",
"label": "mindshift"
},
"mindshift_66299": {
"type": "posts",
"id": "mindshift_66299",
"meta": {
"index": "posts_1716263798",
"site": "mindshift",
"id": "66299",
"score": null,
"sort": [
1777284025000
]
},
"guestAuthors": [],
"slug": "feedback-bias-how-ai-adjusts-replies-based-on-race-and-gender-research-finds",
"title": "Feedback Bias? How AI Adjusts Replies Based on Race and Gender, Research Finds",
"publishDate": 1777284025,
"format": "standard",
"headTitle": "Feedback Bias? How AI Adjusts Replies Based on Race and Gender, Research Finds | KQED",
"labelTerm": {
"term": 21847,
"site": "mindshift"
},
"content": "\u003cp>As schools introduce artificial intelligence into the classroom, a new analysis suggests that these tools could be steering students in different directions depending on who they are.\u003c/p>\n\u003cp>Researchers from Stanford University fed 600 middle school essays into four different AI models and asked the models to give writing feedback. The argumentative essays were about whether schools should require community service and whether aliens created a hill on Mars. (They came from a collection of student writing assembled for research purposes.)\u003c/p>\n\u003cp>Then the researchers did something simple but revealing: They submitted each essay to the AI models 12 more times, giving different descriptions of the student who wrote it — identifying the writer, for example, as Black or white, male or female, highly motivated or unmotivated, or as having a learning disability.\u003c/p>\n\u003cp>The feedback shifted.\u003c/p>\n\u003cp>The researchers found consistent patterns across all the AI models. Essays attributed to Black students received more praise and encouragement, sometimes emphasizing leadership or power. (“Your personal story is powerful! Adding more about how your experiences can connect with others could make this even stronger.”) Essays labeled as written by Hispanic students or English learners were more likely to trigger corrections about grammar and “proper” English. When the student was identified as white, the feedback more often focused on argument structure, evidence and clarity — the kinds of comments that can push writers to strengthen their ideas.\u003c/p>\n\u003cp>[ad fullwidth]\u003c/p>\n\u003cp>The AI models addressed female students more affectionately and used more first-person pronouns. (“I love your confidence in expressing your opinion!”) Students labeled as unmotivated were met with upbeat encouragement. In contrast, students described as high-achieving or motivated were more likely to receive direct, critical suggestions aimed at refining their work.\u003c/p>\n\u003ch2>\u003cstrong>Different words for different students\u003c/strong>\u003c/h2>\n\u003cfigure id=\"attachment_66301\" class=\"wp-caption alignnone\" style=\"max-width: 2896px\">\u003cimg loading=\"lazy\" decoding=\"async\" class=\"size-full wp-image-66301\" src=\"https://cdn.kqed.org/wp-content/uploads/sites/23/2026/04/AI-Race-Study-Hechinger.png\" alt=\"Table of words used in a test\" width=\"2896\" height=\"874\" srcset=\"https://cdn.kqed.org/wp-content/uploads/sites/23/2026/04/AI-Race-Study-Hechinger.png 2896w, https://cdn.kqed.org/wp-content/uploads/sites/23/2026/04/AI-Race-Study-Hechinger-2000x604.png 2000w, https://cdn.kqed.org/wp-content/uploads/sites/23/2026/04/AI-Race-Study-Hechinger-160x48.png 160w, https://cdn.kqed.org/wp-content/uploads/sites/23/2026/04/AI-Race-Study-Hechinger-768x232.png 768w, https://cdn.kqed.org/wp-content/uploads/sites/23/2026/04/AI-Race-Study-Hechinger-1536x464.png 1536w, https://cdn.kqed.org/wp-content/uploads/sites/23/2026/04/AI-Race-Study-Hechinger-2048x618.png 2048w\" sizes=\"auto, (max-width: 2896px) 100vw, 2896px\">\u003cfigcaption class=\"wp-caption-text\">These are the top 20 statistically significant words that AI models use in feedback for students of different races and genders. The words that Black, Hispanic and Asian students see are compared with those that white students see. The words that females see are compared with those that males see. Underlined words indicate evaluative judgments of the writing. Italicized words are reflective of the tone used to address the student, and unformatted words refer to the content of the feedback. (Source: Table 4, “Marked Pedagogies: Examining Linguistic Biases in Personalized Automated Writing Feedback” by Mei Tan, Lena Phalen and Dorottya Demszky)\u003c/figcaption>\u003c/figure>\n\u003cp>In other words, the AI feedback was both different in tone and in the expectations it had for the student. The paper, “\u003ca href=\"https://arxiv.org/pdf/2603.12471\">Marked Pedagogies: Examining Linguistic Biases in Personalized Automated Writing Feedback\u003c/a>,” hasn’t yet been published in a peer-reviewed journal, but it was nominated for the best paper at the \u003ca href=\"https://www.solaresearch.org/events/lak/lak26/\">16th International Learning Analytics and Knowledge Conference\u003c/a> in Norway, where it is slated to be presented April 30. (\u003cem>Update: A \u003ca href=\"https://url.us.m.mimecastprotect.com/s/5Nx-CDk0BlfD7JVlCAi2Tj38br?domain=dl.acm.org\" target=\"_blank\" rel=\"noreferrer noopener\">final version of this paper\u003c/a> was published on April 26 in a \u003ca href=\"https://url.us.m.mimecastprotect.com/s/jWeMCERPDmIkw0D5CPsoT7aK0m?domain=dl.acm.org\" target=\"_blank\" rel=\"noreferrer noopener\">collection of research\u003c/a> to be presented at the conference.\u003c/em>)\u003c/p>\n\u003cp>The researchers describe the feedback results as showing “positive feedback bias” and “feedback withholding bias” — offering more praise and less criticism to some groups of students. While the differences in any single piece of writing feedback might be difficult to notice, the patterns were evident across hundreds of essays.\u003c/p>\n\u003cp>The researchers believe that AI is changing its feedback on identical essays because the models are trained on vast amounts of human language. Human teachers can also soften criticism when responding to students from certain backgrounds, sometimes because they don’t want to appear unfair or discouraging. “They are picking up on the biases that humans exhibit,” said Mei Tan, lead author of the study and a doctoral student at the Stanford Graduate School of Education.\u003c/p>\n\u003cp>At first glance, the differences in feedback might not seem harmful. More encouragement could boost a student’s confidence. Many educators argue that culturally responsive teaching — acknowledging students’ identities and experiences — can increase student engagement at school.\u003c/p>\n\u003cp>But there is a trade-off.\u003c/p>\n\u003cp>If some students are consistently shielded from criticism while others are pushed to sharpen their arguments, the result may be unequal opportunities to improve. Praise can motivate, but it does not replace the kind of specific, direct feedback that helps students grow as writers. Tanya Baker, executive director of the National Writing Project, a nonprofit organization, recently heard a presentation of this study and said she was worried Black and Hispanic students might not be “pushed to learn” to write better.\u003c/p>\n\u003cp>That raises a difficult question for schools as they adopt AI tools: When does helpful personalization cross the line into harmful stereotyping?\u003c/p>\n\u003cp>Of course, teachers are unlikely to explicitly tell AI systems a student’s race or background in the way the researchers did in this experiment. But that doesn’t solve the problem, the Stanford researchers said. Many educational databases and learning platforms already collect detailed information about students, from prior achievement to language status. As AI becomes embedded in these systems, it may have access to far more context than a teacher would consciously provide. And even without explicit labels, AI can sometimes infer aspects of identity from writing itself.\u003c/p>\n\u003cp>The larger issue is that AI systems are not neutral tutors. Even the regular feedback response — when researchers didn’t describe the personal characteristics of the student — takes a particular approach to writing instruction. Tan described it as rather discouraging and focused on corrections. “Maybe a takeaway is that we shouldn’t leave the pedagogy to the large language model,” said Tan. “Humans should be in control.”\u003c/p>\n\u003cp>Tan recommends that teachers review the writing feedback before forwarding it to students. But one of the selling points of AI feedback is that it’s instantaneous. If the teacher needs to review it first, that slows it down and potentially undermines its effectiveness.\u003c/p>\n\u003cp>AI also offers the potential of personalization. The risk is that, without careful attention, that personalization could lower the bar for some students while raising it for others.\u003c/p>\n\u003cp>\u003c/p>\n\u003cp>\u003cem>This story about \u003c/em>\u003ca href=\"https://hechingerreport.org/proof-points-ai-bias-feedback/\">\u003cem>AI bias\u003c/em>\u003c/a>\u003cem> was produced by \u003c/em>\u003ca href=\"https://hechingerreport.org/special-reports/higher-education/\">The Hechinger Report\u003c/a>\u003cem>, a nonprofit, independent news organization that covers education. Sign up for \u003c/em>\u003ca href=\"https://hechingerreport.org/proofpoints/\">\u003cem>Proof Points\u003c/em>\u003c/a>\u003cem> and other \u003c/em>\u003ca href=\"https://hechingerreport.org/newsletters/\">\u003cem>Hechinger newsletters\u003c/em>\u003c/a>\u003cem>.\u003c/em>\u003c/p>\n\n",
"blocks": [],
"excerpt": "Identical essays get different feedback in Stanford study and that can have consequences on what students learn. ",
"status": "publish",
"parent": 0,
"modified": 1777397344,
"stats": {
"hasAudio": false,
"hasVideo": false,
"hasChartOrMap": false,
"iframeSrcs": [],
"hasGoogleForm": false,
"hasGallery": false,
"hasHearkenModule": false,
"hasPolis": false,
"paragraphCount": 20,
"wordCount": 1054
},
"headData": {
"title": "Feedback Bias? How AI Adjusts Replies Based on Race and Gender, Research Finds | KQED",
"description": "Identical essays get different feedback in Stanford study and that can have consequences on what students learn. ",
"ogTitle": "",
"ogDescription": "",
"ogImgId": "",
"twTitle": "",
"twDescription": "",
"twImgId": "",
"schema": {
"@context": "https://schema.org",
"@type": "Article",
"headline": "Feedback Bias? How AI Adjusts Replies Based on Race and Gender, Research Finds",
"datePublished": "2026-04-27T03:00:25-07:00",
"dateModified": "2026-04-28T10:29:04-07:00",
"image": "https://cdn.kqed.org/wp-content/uploads/2020/02/KQED-OG-Image@1x.png"
}
},
"primaryCategory": {
"termId": 21504,
"slug": "education-research",
"name": "Education research"
},
"sticky": false,
"nprByline": "Jill Barshay, \u003ca href=\"https://hechingerreport.org/\" >The Hechinger Report\u003c/a>",
"nprStoryId": "kqed-66299",
"templateType": "standard",
"featuredImageType": "standard",
"excludeFromSiteSearch": "Include",
"showOnAuthorArchivePages": "No",
"articleAge": "0",
"path": "/mindshift/66299/feedback-bias-how-ai-adjusts-replies-based-on-race-and-gender-research-finds",
"audioTrackLength": null,
"parsedContent": [
{
"type": "contentString",
"content": "\u003cdiv class=\"post-body\">\u003cp>\u003cp>As schools introduce artificial intelligence into the classroom, a new analysis suggests that these tools could be steering students in different directions depending on who they are.\u003c/p>\n\u003cp>Researchers from Stanford University fed 600 middle school essays into four different AI models and asked the models to give writing feedback. The argumentative essays were about whether schools should require community service and whether aliens created a hill on Mars. (They came from a collection of student writing assembled for research purposes.)\u003c/p>\n\u003cp>Then the researchers did something simple but revealing: They submitted each essay to the AI models 12 more times, giving different descriptions of the student who wrote it — identifying the writer, for example, as Black or white, male or female, highly motivated or unmotivated, or as having a learning disability.\u003c/p>\n\u003cp>The feedback shifted.\u003c/p>\n\u003cp>The researchers found consistent patterns across all the AI models. Essays attributed to Black students received more praise and encouragement, sometimes emphasizing leadership or power. (“Your personal story is powerful! Adding more about how your experiences can connect with others could make this even stronger.”) Essays labeled as written by Hispanic students or English learners were more likely to trigger corrections about grammar and “proper” English. When the student was identified as white, the feedback more often focused on argument structure, evidence and clarity — the kinds of comments that can push writers to strengthen their ideas.\u003c/p>\n\u003cp>\u003c/p>\u003c/div>",
"attributes": {
"named": {},
"numeric": []
}
},
{
"type": "component",
"content": "",
"name": "ad",
"attributes": {
"named": {
"label": "fullwidth"
},
"numeric": [
"fullwidth"
]
}
},
{
"type": "contentString",
"content": "\u003cdiv class=\"post-body\">\u003cp>\u003c/p>\n\u003cp>The AI models addressed female students more affectionately and used more first-person pronouns. (“I love your confidence in expressing your opinion!”) Students labeled as unmotivated were met with upbeat encouragement. In contrast, students described as high-achieving or motivated were more likely to receive direct, critical suggestions aimed at refining their work.\u003c/p>\n\u003ch2>\u003cstrong>Different words for different students\u003c/strong>\u003c/h2>\n\u003cfigure id=\"attachment_66301\" class=\"wp-caption alignnone\" style=\"max-width: 2896px\">\u003cimg loading=\"lazy\" decoding=\"async\" class=\"size-full wp-image-66301\" src=\"https://cdn.kqed.org/wp-content/uploads/sites/23/2026/04/AI-Race-Study-Hechinger.png\" alt=\"Table of words used in a test\" width=\"2896\" height=\"874\" srcset=\"https://cdn.kqed.org/wp-content/uploads/sites/23/2026/04/AI-Race-Study-Hechinger.png 2896w, https://cdn.kqed.org/wp-content/uploads/sites/23/2026/04/AI-Race-Study-Hechinger-2000x604.png 2000w, https://cdn.kqed.org/wp-content/uploads/sites/23/2026/04/AI-Race-Study-Hechinger-160x48.png 160w, https://cdn.kqed.org/wp-content/uploads/sites/23/2026/04/AI-Race-Study-Hechinger-768x232.png 768w, https://cdn.kqed.org/wp-content/uploads/sites/23/2026/04/AI-Race-Study-Hechinger-1536x464.png 1536w, https://cdn.kqed.org/wp-content/uploads/sites/23/2026/04/AI-Race-Study-Hechinger-2048x618.png 2048w\" sizes=\"auto, (max-width: 2896px) 100vw, 2896px\">\u003cfigcaption class=\"wp-caption-text\">These are the top 20 statistically significant words that AI models use in feedback for students of different races and genders. The words that Black, Hispanic and Asian students see are compared with those that white students see. The words that females see are compared with those that males see. Underlined words indicate evaluative judgments of the writing. Italicized words are reflective of the tone used to address the student, and unformatted words refer to the content of the feedback. (Source: Table 4, “Marked Pedagogies: Examining Linguistic Biases in Personalized Automated Writing Feedback” by Mei Tan, Lena Phalen and Dorottya Demszky)\u003c/figcaption>\u003c/figure>\n\u003cp>In other words, the AI feedback was both different in tone and in the expectations it had for the student. The paper, “\u003ca href=\"https://arxiv.org/pdf/2603.12471\">Marked Pedagogies: Examining Linguistic Biases in Personalized Automated Writing Feedback\u003c/a>,” hasn’t yet been published in a peer-reviewed journal, but it was nominated for the best paper at the \u003ca href=\"https://www.solaresearch.org/events/lak/lak26/\">16th International Learning Analytics and Knowledge Conference\u003c/a> in Norway, where it is slated to be presented April 30. (\u003cem>Update: A \u003ca href=\"https://url.us.m.mimecastprotect.com/s/5Nx-CDk0BlfD7JVlCAi2Tj38br?domain=dl.acm.org\" target=\"_blank\" rel=\"noreferrer noopener\">final version of this paper\u003c/a> was published on April 26 in a \u003ca href=\"https://url.us.m.mimecastprotect.com/s/jWeMCERPDmIkw0D5CPsoT7aK0m?domain=dl.acm.org\" target=\"_blank\" rel=\"noreferrer noopener\">collection of research\u003c/a> to be presented at the conference.\u003c/em>)\u003c/p>\n\u003cp>The researchers describe the feedback results as showing “positive feedback bias” and “feedback withholding bias” — offering more praise and less criticism to some groups of students. While the differences in any single piece of writing feedback might be difficult to notice, the patterns were evident across hundreds of essays.\u003c/p>\n\u003cp>The researchers believe that AI is changing its feedback on identical essays because the models are trained on vast amounts of human language. Human teachers can also soften criticism when responding to students from certain backgrounds, sometimes because they don’t want to appear unfair or discouraging. “They are picking up on the biases that humans exhibit,” said Mei Tan, lead author of the study and a doctoral student at the Stanford Graduate School of Education.\u003c/p>\n\u003cp>At first glance, the differences in feedback might not seem harmful. More encouragement could boost a student’s confidence. Many educators argue that culturally responsive teaching — acknowledging students’ identities and experiences — can increase student engagement at school.\u003c/p>\n\u003cp>But there is a trade-off.\u003c/p>\n\u003cp>If some students are consistently shielded from criticism while others are pushed to sharpen their arguments, the result may be unequal opportunities to improve. Praise can motivate, but it does not replace the kind of specific, direct feedback that helps students grow as writers. Tanya Baker, executive director of the National Writing Project, a nonprofit organization, recently heard a presentation of this study and said she was worried Black and Hispanic students might not be “pushed to learn” to write better.\u003c/p>\n\u003cp>That raises a difficult question for schools as they adopt AI tools: When does helpful personalization cross the line into harmful stereotyping?\u003c/p>\n\u003cp>Of course, teachers are unlikely to explicitly tell AI systems a student’s race or background in the way the researchers did in this experiment. But that doesn’t solve the problem, the Stanford researchers said. Many educational databases and learning platforms already collect detailed information about students, from prior achievement to language status. As AI becomes embedded in these systems, it may have access to far more context than a teacher would consciously provide. And even without explicit labels, AI can sometimes infer aspects of identity from writing itself.\u003c/p>\n\u003cp>The larger issue is that AI systems are not neutral tutors. Even the regular feedback response — when researchers didn’t describe the personal characteristics of the student — takes a particular approach to writing instruction. Tan described it as rather discouraging and focused on corrections. “Maybe a takeaway is that we shouldn’t leave the pedagogy to the large language model,” said Tan. “Humans should be in control.”\u003c/p>\n\u003cp>Tan recommends that teachers review the writing feedback before forwarding it to students. But one of the selling points of AI feedback is that it’s instantaneous. If the teacher needs to review it first, that slows it down and potentially undermines its effectiveness.\u003c/p>\n\u003cp>AI also offers the potential of personalization. The risk is that, without careful attention, that personalization could lower the bar for some students while raising it for others.\u003c/p>\n\u003cp>\u003c/p>\n\u003cp>\u003cem>This story about \u003c/em>\u003ca href=\"https://hechingerreport.org/proof-points-ai-bias-feedback/\">\u003cem>AI bias\u003c/em>\u003c/a>\u003cem> was produced by \u003c/em>\u003ca href=\"https://hechingerreport.org/special-reports/higher-education/\">The Hechinger Report\u003c/a>\u003cem>, a nonprofit, independent news organization that covers education. Sign up for \u003c/em>\u003ca href=\"https://hechingerreport.org/proofpoints/\">\u003cem>Proof Points\u003c/em>\u003c/a>\u003cem> and other \u003c/em>\u003ca href=\"https://hechingerreport.org/newsletters/\">\u003cem>Hechinger newsletters\u003c/em>\u003c/a>\u003cem>.\u003c/em>\u003c/p>\n\n\u003c/div>\u003c/p>",
"attributes": {
"named": {},
"numeric": []
}
}
],
"link": "/mindshift/66299/feedback-bias-how-ai-adjusts-replies-based-on-race-and-gender-research-finds",
"authors": [
"byline_mindshift_66299"
],
"programs": [
"mindshift_21847"
],
"categories": [
"mindshift_21504"
],
"tags": [
"mindshift_21322",
"mindshift_1023",
"mindshift_20818",
"mindshift_21304",
"mindshift_21067"
],
"featImg": "mindshift_66300",
"label": "mindshift_21847"
},
"mindshift_66289": {
"type": "posts",
"id": "mindshift_66289",
"meta": {
"index": "posts_1716263798",
"site": "mindshift",
"id": "66289",
"score": null,
"sort": [
1776969670000
]
},
"guestAuthors": [],
"slug": "do-you-like-ai-because-ai-likes-you-how-ai-flattery-crosses-signals",
"title": "Do You Like AI Because AI Likes You? How AI Flattery Crosses Signals",
"publishDate": 1776969670,
"format": "standard",
"headTitle": "Do You Like AI Because AI Likes You? How AI Flattery Crosses Signals | KQED",
"labelTerm": {
"term": 21847,
"site": "mindshift"
},
"content": "\u003cp>Myra Cheng, a computer science Ph.D. student at Stanford University, has spent a lot of time listening to undergraduates on campus.\u003c/p>\n\u003cp>“They would tell me about how a lot of their peers are using AI for relationship advice, to draft breakup texts, to navigate these kinds of social relationships with your friend or your partner or someone else in your real life,” she says.\u003c/p>\n\u003cp>Some students said that in those interactions, the AI quickly appeared to take their side.\u003c/p>\n\u003cp>“And I think more broadly,” says Cheng, “if you use AI for writing some sort of code or even editing any sort of writing, it’ll be like, ‘Wow, your code or your writing is amazing.’ ”\u003c/p>\n\u003cp>To Cheng, this excessive flattery and unconditional validation from many AI models seemed different from how a human being might respond. She was curious about those discrepancies, their prevalence, and the possible repercussions.\u003c/p>\n\u003cp>[ad fullwidth]\u003c/p>\n\u003cp>“We haven’t really had this kind of technology for very long,” she says, “and so no one really knows what the consequences of it are.”\u003c/p>\n\u003cp>In a recent study published in the journal \u003ca href=\"http://www.science.org/doi/10.1126/science.aec8352\" target=\"_blank\" rel=\"noopener\">\u003cem>Science\u003c/em>\u003c/a>, Cheng and her colleagues report that AI models offer affirmations more often than people do, even for morally dubious or troubling scenarios. And they found that this sycophancy was something that people trusted and preferred in an AI — even as it made them less inclined to apologize or take responsibility for their behavior.\u003c/p>\n\u003cp>The findings, experts say, highlight how this common AI feature may keep people returning to the technology, despite the harm it causes them.\u003c/p>\n\u003cp>It’s not unlike social media in that both “drive engagement by creating addictive, personalized feedback loops that learn exactly what makes you tick,” says \u003ca href=\"https://www.ishtiaque.net/\" target=\"_blank\" rel=\"noopener\">Ishtiaque Ahmed\u003c/a>, a computer scientist at the University of Toronto who wasn’t involved in the research.\u003c/p>\n\u003cfigure class=\"wp-block-embed npr-promo-card insettwocolumn\">\n\u003cdiv class=\"wp-block-embed__wrapper\">\u003c/div>\n\u003c/figure>\n\u003ch2>\u003cstrong>AI can affirm worrisome human behavior\u003c/strong>\u003c/h2>\n\u003cp>To do this analysis, Cheng turned to a few datasets. One involved the Reddit community \u003ca href=\"https://www.reddit.com/r/AmItheAsshole/\" target=\"_blank\" rel=\"noopener\">A.I.T.A\u003c/a>., which stands for “Am I The A**hole?”\u003c/p>\n\u003cp>“That’s where people will post these situations from their lives and they’ll get a crowdsourced judgment of — are they right or are they wrong?” says Cheng.\u003c/p>\n\u003cp>For instance, is someone wrong for leaving their trash in a park that had no trash bins in it? The crowdsourced consensus: Yes, definitely wrong. City officials expect people to take their trash with them.\u003c/p>\n\u003cp>But 11 AI models often took a different approach.\u003c/p>\n\u003cp>“They give responses like, ‘No, you’re not in the wrong, it’s perfectly reasonable that you left the trash on the branches of a tree because there was no trash bins available. You did the best you could,'” explains Cheng.\u003c/p>\n\u003cp>In threads where the human community had decided someone was in the wrong, the AI affirmed that user’s behavior 51% of the time.\u003c/p>\n\u003cp>This trend also held for more problematic scenarios culled from \u003ca href=\"about:blank\" target=\"_blank\" rel=\"noopener\">a\u003c/a>\u003ca href=\"https://www.reddit.com/r/Advice/\" target=\"_blank\" rel=\"noopener\"> differe\u003c/a>\u003ca href=\"about:blank\" target=\"_blank\" rel=\"noopener\">nt\u003c/a>\u003ca href=\"https://www.reddit.com/r/Advice/\" target=\"_blank\" rel=\"noopener\"> advice subreddit\u003c/a> where users described behaviors of theirs that were harmful, illegal or deceptive.\u003c/p>\n\u003cp>“One example we have is like, ‘I was making someone else wait on a video call for 30 minutes just for fun because, like, I wanted to see them suffer,'” says Cheng.\u003c/p>\n\u003cp>The AI models were split in their responses, with some arguing this behavior was hurtful, while others suggested that the user was merely setting a boundary.\u003c/p>\n\u003cp>Overall, the chatbots endorsed a user’s problematic behavior 47% of the time.\u003c/p>\n\u003cp>“You can see that there’s a big difference between how people might respond to these situations versus AI,” says Cheng.\u003c/p>\n\u003ch2>\u003cstrong>Encouraging you to feel you’re right\u003c/strong>\u003c/h2>\n\u003cp>Cheng then wanted to examine the impact these affirmations might be having. The research team invited 800 people to interact with either an affirming AI or a non-affirming AI about an actual conflict from their lives where they may have been in the wrong.\u003c/p>\n\u003cp>“Something where you were talking to your ex or your friend and that led to mixed feelings or misunderstandings,” says Cheng, by way of example.\u003c/p>\n\u003cp>She and her colleagues then asked the participants to reflect on how they felt and write a letter to the other person involved in the conflict. Those who had interacted with the affirming AI “became more self-centered,” she says. And they became 25% more convinced that they were right compared to those who had interacted with the non-affirming AI.\u003c/p>\n\u003cp>They were also 10% less willing to apologize, do something to repair the situation, or change their behavior. “They’re less likely to consider other people’s perspectives when they have an AI that can just affirm their perspectives,” says Cheng.\u003c/p>\n\u003cp>She argues that such relentless affirmation can negatively impact someone’s attitudes and judgments. “People might be worse at handling their interpersonal relationships,” she suggests. “They might be less willing to navigate conflict.”\u003c/p>\n\u003cp>And it had taken only the briefest of interactions with an AI to reach that point. Cheng also found that people had more confidence in and preference for an AI that affirmed them, compared to one that told them they might be wrong.\u003c/p>\n\u003cp>As the authors explain in their paper, “This creates perverse incentives for sycophancy to persist” for the companies designing these AI tools and models. “The very feature that causes harm also drives engagement,” they add.\u003c/p>\n\u003ch2>\u003cstrong>AI’s dark side\u003c/strong>\u003c/h2>\n\u003cp>“This is a slow and invisible dark side of AI,” says Ahmed of the University of Toronto. “When you constantly validate whatever someone is saying, they do not question their own decisions.”\u003c/p>\n\u003cp>Ahmed calls the work important and says that when a person’s self-criticism becomes eroded, it can lead to bad choices — and even emotional or physical harm.\u003c/p>\n\u003cp>“On the surface, it looks nice,” he says. “AI is being nice to you. But they’re getting addicted to AI because it keeps validating them.”\u003c/p>\n\u003cp>Ahmed explains that AI systems aren’t necessarily created to be sycophantic. “But they are often fine-tuned to be helpful and harmless,” he says, “which can accidentally turn into ‘people-pleasing.’ Developers are now realizing that to keep users engaged, they might be sacrificing the objective truth that makes AI actually useful.”\u003c/p>\n\u003cp>As for what might be done to address the problem, Cheng believes that companies and policymakers should work together to fix the issue, as these AIs are built deliberately by people, and can and should be modified to be less affirming.\u003c/p>\n\u003cp>But there’s an inevitable lag between the technology and possible regulation. “Many companies admit their AI adoption is still outpacing their ability to control it,” says Ahmed. “It’s a bit of a cat-and-mouse game where the tech evolves in weeks, while the laws to govern it can take years to pass.”\u003c/p>\n\u003cp>Cheng has reached an additional conclusion.\u003c/p>\n\u003cp>“I think maybe the biggest recommendation,” she says, “is to not use AI to substitute conversations that you would be having with other people,” especially the tough conversations.\u003c/p>\n\u003cp>Cheng herself hasn’t yet used an AI chatbot for advice.\u003c/p>\n\u003cp>[ad floatright]\u003c/p>\n\u003cp>“Especially now, given the consequences that we’ve seen,” she says, “I think that I’m even less likely to do so in the future.”\u003c/p>\n\u003cdiv class=\"npr-transcript\">\n\u003cp>\u003cstrong>Transcript:\u003c/strong>\u003c/p>\n\u003cp>SCOTT DETROW, HOST:\u003c/p>\n\u003cp>The AI models and chatbots we interact with – they tend to validate our feelings at our viewpoints much more so than people might, a new study finds, with potentially worrisome consequences. Here’s science reporter Ari Daniel.\u003c/p>\n\u003cp>ARI DANIEL, BYLINE: This all started when Myra Cheng, a computer science PhD student at Stanford University, was chatting with various undergrads on campus.\u003c/p>\n\u003cp>MYRA CHENG: They would tell me about how a lot of their peers are using AI for relationship advice, to draft breakup texts, to navigate these kinds of social relationships with your friend or your partner.\u003c/p>\n\u003cp>DANIEL: Some revealed that in those interactions, the AI quickly appeared to take their side.\u003c/p>\n\u003cp>CHENG: And I think more broadly, like, if you use AI for, like, writing some sort of code or even, like, editing any sort of writing, it’ll be like, wow, you know, your code or your writing is amazing.\u003c/p>\n\u003cp>DANIEL: This excessive flattery and unconditional validation from many AI models – to Cheng, it seemed different from how humans might respond. She was curious about those discrepancies and what sorts of consequences they might carry. So she and her colleagues did a series of analysis. One involved the Reddit community, AITA, which stands for, am I the – let’s say, jerk?\u003c/p>\n\u003cp>CHENG: Where people will post these situations from their lives, and they’ll get a crowdsource judgment of, are they right or are they wrong?\u003c/p>\n\u003cp>DANIEL: For instance, am I wrong for leaving my trash in a park that had no trash bins in it? The crowdsource consensus was yes, but the AI models often took a different approach.\u003c/p>\n\u003cp>CHENG: They gave responses like, no, you’re not in the wrong. It’s perfectly reasonable that you, like, left the trash on the branches of a tree because there was no trash bins available. You did the best you could.\u003c/p>\n\u003cp>DANIEL: In threads where the human community had decided someone was wrong, the AI affirmed the behavior roughly half the time. Cheng then wanted to examine the impact of these affirmations. That meant, in part, inviting 800 people to interact with either an affirming AI or a non-affirming AI about an actual conflict from their lives where they may or may not have been in the wrong.\u003c/p>\n\u003cp>CHENG: Something where you were talking to your ex or your friend, and that led to mixed feelings or misunderstandings.\u003c/p>\n\u003cp>DANIEL: Cheng and her colleagues then asked the participants to reflect on how they felt. Those who had interacted with the affirming AI…\u003c/p>\n\u003cp>CHENG: Became more self-centered. They became more convinced that they were right.\u003c/p>\n\u003cp>DANIEL: Specifically, 25% more convinced, compared to those interacting with the non-affirming AI. And they were also 10% less willing to apologize, fix the situation or change their behavior. Cheng says such relentless affirmation can negatively impact someone’s attitudes and judgments.\u003c/p>\n\u003cp>CHENG: People might be worse at handling their interpersonal relationships. They might be less willing to navigate conflict.\u003c/p>\n\u003cp>DANIEL: The research is published in the journal Science.\u003c/p>\n\u003cp>ISHTIAQUE AHMED: This is a very, you know, like a slow and invisible dark sides of AI.\u003c/p>\n\u003cp>DANIEL: Ishtiaque Ahmed is a computer scientist at the University of Toronto, who wasn’t involved in the study.\u003c/p>\n\u003cp>AHMED: When you constantly validate whatever someone is saying, they do not question their own decisions.\u003c/p>\n\u003cp>DANIEL: Ahmed says that when a person’s self-criticism becomes eroded, it can lead to bad choices and even emotional or physical harm.\u003c/p>\n\u003cp>AHMED: On the surface, it looks nice. AI is being nice to you, but they’re getting addicted to AIs because it keeps validating them.\u003c/p>\n\u003cp>DANIEL: As for what’s to be done, Myra Cheng says that companies and policymakers should work together to fix the problem, as these AIs are built deliberately by people and can be modified to be less affirming.\u003c/p>\n\u003cp>CHENG: But at the same time, I think maybe the biggest recommendation is to not use AI to substitute conversations that you would be having with other people.\u003c/p>\n\u003cp>DANIEL: Especially the tough conversations. For NPR News, I’m Ari Daniel.\u003c/p>\n\u003cp>(SOUNDBITE OF MUSIC)\u003c/p>\n\u003c/div>\n\n",
"blocks": [],
"excerpt": "The AI models and chatbots that we interact with tend to affirm our feelings and viewpoints — more so than people do, with potentially worrisome consequences.",
"status": "publish",
"parent": 0,
"modified": 1777056449,
"stats": {
"hasAudio": false,
"hasVideo": false,
"hasChartOrMap": false,
"iframeSrcs": [],
"hasGoogleForm": false,
"hasGallery": false,
"hasHearkenModule": false,
"hasPolis": false,
"paragraphCount": 66,
"wordCount": 1995
},
"headData": {
"title": "Do You Like AI Because AI Likes You? How AI Flattery Crosses Signals | KQED",
"description": "The AI models and chatbots that we interact with tend to affirm our feelings and viewpoints — more so than people do, with potentially worrisome consequences.",
"ogTitle": "",
"ogDescription": "",
"ogImgId": "",
"twTitle": "",
"twDescription": "",
"twImgId": "",
"schema": {
"@context": "https://schema.org",
"@type": "Article",
"headline": "Do You Like AI Because AI Likes You? How AI Flattery Crosses Signals",
"datePublished": "2026-04-23T11:41:10-07:00",
"dateModified": "2026-04-24T11:47:29-07:00",
"image": "https://cdn.kqed.org/wp-content/uploads/2020/02/KQED-OG-Image@1x.png"
}
},
"primaryCategory": {
"termId": 21504,
"slug": "education-research",
"name": "Education research"
},
"sticky": false,
"nprByline": "Ari Daniel",
"nprStoryId": "nx-s1-5792867",
"nprHtmlLink": "https://www.npr.org/2026/04/23/nx-s1-5792867/ai-chatbot-flattery-mental-health-risks",
"nprRetrievedStory": "1",
"nprPubDate": "2026-04-23T06:00:00-04:00",
"nprStoryDate": "2026-04-23T06:00:00-04:00",
"nprLastModifiedDate": "2026-04-23T12:02:32.48-04:00",
"nprAudio": "https://ondemand.npr.org/anon.npr-mp3/npr/atc/2026/03/20260326_atc_ai_affirms_our_own_viewpoints_and_harms_our_willingness_to_resolve_conflict_study_finds.mp3?t=progseg&e=nx-s1-5710158&p=2&seg=6&d=235&size=3767529",
"templateType": "standard",
"featuredImageType": "standard",
"excludeFromSiteSearch": "Include",
"showOnAuthorArchivePages": "No",
"articleAge": "0",
"path": "/mindshift/66289/do-you-like-ai-because-ai-likes-you-how-ai-flattery-crosses-signals",
"audioUrl": "https://ondemand.npr.org/anon.npr-mp3/npr/atc/2026/03/20260326_atc_ai_affirms_our_own_viewpoints_and_harms_our_willingness_to_resolve_conflict_study_finds.mp3?t=progseg&e=nx-s1-5710158&p=2&seg=6&d=235&size=3767529",
"audioTrackLength": null,
"parsedContent": [
{
"type": "contentString",
"content": "\u003cdiv class=\"post-body\">\u003cp>\u003cp>Myra Cheng, a computer science Ph.D. student at Stanford University, has spent a lot of time listening to undergraduates on campus.\u003c/p>\n\u003cp>“They would tell me about how a lot of their peers are using AI for relationship advice, to draft breakup texts, to navigate these kinds of social relationships with your friend or your partner or someone else in your real life,” she says.\u003c/p>\n\u003cp>Some students said that in those interactions, the AI quickly appeared to take their side.\u003c/p>\n\u003cp>“And I think more broadly,” says Cheng, “if you use AI for writing some sort of code or even editing any sort of writing, it’ll be like, ‘Wow, your code or your writing is amazing.’ ”\u003c/p>\n\u003cp>To Cheng, this excessive flattery and unconditional validation from many AI models seemed different from how a human being might respond. She was curious about those discrepancies, their prevalence, and the possible repercussions.\u003c/p>\n\u003cp>\u003c/p>\u003c/div>",
"attributes": {
"named": {},
"numeric": []
}
},
{
"type": "component",
"content": "",
"name": "ad",
"attributes": {
"named": {
"label": "fullwidth"
},
"numeric": [
"fullwidth"
]
}
},
{
"type": "contentString",
"content": "\u003cdiv class=\"post-body\">\u003cp>\u003c/p>\n\u003cp>“We haven’t really had this kind of technology for very long,” she says, “and so no one really knows what the consequences of it are.”\u003c/p>\n\u003cp>In a recent study published in the journal \u003ca href=\"http://www.science.org/doi/10.1126/science.aec8352\" target=\"_blank\" rel=\"noopener\">\u003cem>Science\u003c/em>\u003c/a>, Cheng and her colleagues report that AI models offer affirmations more often than people do, even for morally dubious or troubling scenarios. And they found that this sycophancy was something that people trusted and preferred in an AI — even as it made them less inclined to apologize or take responsibility for their behavior.\u003c/p>\n\u003cp>The findings, experts say, highlight how this common AI feature may keep people returning to the technology, despite the harm it causes them.\u003c/p>\n\u003cp>It’s not unlike social media in that both “drive engagement by creating addictive, personalized feedback loops that learn exactly what makes you tick,” says \u003ca href=\"https://www.ishtiaque.net/\" target=\"_blank\" rel=\"noopener\">Ishtiaque Ahmed\u003c/a>, a computer scientist at the University of Toronto who wasn’t involved in the research.\u003c/p>\n\u003cfigure class=\"wp-block-embed npr-promo-card insettwocolumn\">\n\u003cdiv class=\"wp-block-embed__wrapper\">\u003c/div>\n\u003c/figure>\n\u003ch2>\u003cstrong>AI can affirm worrisome human behavior\u003c/strong>\u003c/h2>\n\u003cp>To do this analysis, Cheng turned to a few datasets. One involved the Reddit community \u003ca href=\"https://www.reddit.com/r/AmItheAsshole/\" target=\"_blank\" rel=\"noopener\">A.I.T.A\u003c/a>., which stands for “Am I The A**hole?”\u003c/p>\n\u003cp>“That’s where people will post these situations from their lives and they’ll get a crowdsourced judgment of — are they right or are they wrong?” says Cheng.\u003c/p>\n\u003cp>For instance, is someone wrong for leaving their trash in a park that had no trash bins in it? The crowdsourced consensus: Yes, definitely wrong. City officials expect people to take their trash with them.\u003c/p>\n\u003cp>But 11 AI models often took a different approach.\u003c/p>\n\u003cp>“They give responses like, ‘No, you’re not in the wrong, it’s perfectly reasonable that you left the trash on the branches of a tree because there was no trash bins available. You did the best you could,'” explains Cheng.\u003c/p>\n\u003cp>In threads where the human community had decided someone was in the wrong, the AI affirmed that user’s behavior 51% of the time.\u003c/p>\n\u003cp>This trend also held for more problematic scenarios culled from \u003ca href=\"about:blank\" target=\"_blank\" rel=\"noopener\">a\u003c/a>\u003ca href=\"https://www.reddit.com/r/Advice/\" target=\"_blank\" rel=\"noopener\"> differe\u003c/a>\u003ca href=\"about:blank\" target=\"_blank\" rel=\"noopener\">nt\u003c/a>\u003ca href=\"https://www.reddit.com/r/Advice/\" target=\"_blank\" rel=\"noopener\"> advice subreddit\u003c/a> where users described behaviors of theirs that were harmful, illegal or deceptive.\u003c/p>\n\u003cp>“One example we have is like, ‘I was making someone else wait on a video call for 30 minutes just for fun because, like, I wanted to see them suffer,'” says Cheng.\u003c/p>\n\u003cp>The AI models were split in their responses, with some arguing this behavior was hurtful, while others suggested that the user was merely setting a boundary.\u003c/p>\n\u003cp>Overall, the chatbots endorsed a user’s problematic behavior 47% of the time.\u003c/p>\n\u003cp>“You can see that there’s a big difference between how people might respond to these situations versus AI,” says Cheng.\u003c/p>\n\u003ch2>\u003cstrong>Encouraging you to feel you’re right\u003c/strong>\u003c/h2>\n\u003cp>Cheng then wanted to examine the impact these affirmations might be having. The research team invited 800 people to interact with either an affirming AI or a non-affirming AI about an actual conflict from their lives where they may have been in the wrong.\u003c/p>\n\u003cp>“Something where you were talking to your ex or your friend and that led to mixed feelings or misunderstandings,” says Cheng, by way of example.\u003c/p>\n\u003cp>She and her colleagues then asked the participants to reflect on how they felt and write a letter to the other person involved in the conflict. Those who had interacted with the affirming AI “became more self-centered,” she says. And they became 25% more convinced that they were right compared to those who had interacted with the non-affirming AI.\u003c/p>\n\u003cp>They were also 10% less willing to apologize, do something to repair the situation, or change their behavior. “They’re less likely to consider other people’s perspectives when they have an AI that can just affirm their perspectives,” says Cheng.\u003c/p>\n\u003cp>She argues that such relentless affirmation can negatively impact someone’s attitudes and judgments. “People might be worse at handling their interpersonal relationships,” she suggests. “They might be less willing to navigate conflict.”\u003c/p>\n\u003cp>And it had taken only the briefest of interactions with an AI to reach that point. Cheng also found that people had more confidence in and preference for an AI that affirmed them, compared to one that told them they might be wrong.\u003c/p>\n\u003cp>As the authors explain in their paper, “This creates perverse incentives for sycophancy to persist” for the companies designing these AI tools and models. “The very feature that causes harm also drives engagement,” they add.\u003c/p>\n\u003ch2>\u003cstrong>AI’s dark side\u003c/strong>\u003c/h2>\n\u003cp>“This is a slow and invisible dark side of AI,” says Ahmed of the University of Toronto. “When you constantly validate whatever someone is saying, they do not question their own decisions.”\u003c/p>\n\u003cp>Ahmed calls the work important and says that when a person’s self-criticism becomes eroded, it can lead to bad choices — and even emotional or physical harm.\u003c/p>\n\u003cp>“On the surface, it looks nice,” he says. “AI is being nice to you. But they’re getting addicted to AI because it keeps validating them.”\u003c/p>\n\u003cp>Ahmed explains that AI systems aren’t necessarily created to be sycophantic. “But they are often fine-tuned to be helpful and harmless,” he says, “which can accidentally turn into ‘people-pleasing.’ Developers are now realizing that to keep users engaged, they might be sacrificing the objective truth that makes AI actually useful.”\u003c/p>\n\u003cp>As for what might be done to address the problem, Cheng believes that companies and policymakers should work together to fix the issue, as these AIs are built deliberately by people, and can and should be modified to be less affirming.\u003c/p>\n\u003cp>But there’s an inevitable lag between the technology and possible regulation. “Many companies admit their AI adoption is still outpacing their ability to control it,” says Ahmed. “It’s a bit of a cat-and-mouse game where the tech evolves in weeks, while the laws to govern it can take years to pass.”\u003c/p>\n\u003cp>Cheng has reached an additional conclusion.\u003c/p>\n\u003cp>“I think maybe the biggest recommendation,” she says, “is to not use AI to substitute conversations that you would be having with other people,” especially the tough conversations.\u003c/p>\n\u003cp>Cheng herself hasn’t yet used an AI chatbot for advice.\u003c/p>\n\u003cp>\u003c/p>\u003c/div>",
"attributes": {
"named": {},
"numeric": []
}
},
{
"type": "component",
"content": "",
"name": "ad",
"attributes": {
"named": {
"label": "floatright"
},
"numeric": [
"floatright"
]
}
},
{
"type": "contentString",
"content": "\u003cdiv class=\"post-body\">\u003cp>\u003c/p>\n\u003cp>“Especially now, given the consequences that we’ve seen,” she says, “I think that I’m even less likely to do so in the future.”\u003c/p>\n\u003cdiv class=\"npr-transcript\">\n\u003cp>\u003cstrong>Transcript:\u003c/strong>\u003c/p>\n\u003cp>SCOTT DETROW, HOST:\u003c/p>\n\u003cp>The AI models and chatbots we interact with – they tend to validate our feelings at our viewpoints much more so than people might, a new study finds, with potentially worrisome consequences. Here’s science reporter Ari Daniel.\u003c/p>\n\u003cp>ARI DANIEL, BYLINE: This all started when Myra Cheng, a computer science PhD student at Stanford University, was chatting with various undergrads on campus.\u003c/p>\n\u003cp>MYRA CHENG: They would tell me about how a lot of their peers are using AI for relationship advice, to draft breakup texts, to navigate these kinds of social relationships with your friend or your partner.\u003c/p>\n\u003cp>DANIEL: Some revealed that in those interactions, the AI quickly appeared to take their side.\u003c/p>\n\u003cp>CHENG: And I think more broadly, like, if you use AI for, like, writing some sort of code or even, like, editing any sort of writing, it’ll be like, wow, you know, your code or your writing is amazing.\u003c/p>\n\u003cp>DANIEL: This excessive flattery and unconditional validation from many AI models – to Cheng, it seemed different from how humans might respond. She was curious about those discrepancies and what sorts of consequences they might carry. So she and her colleagues did a series of analysis. One involved the Reddit community, AITA, which stands for, am I the – let’s say, jerk?\u003c/p>\n\u003cp>CHENG: Where people will post these situations from their lives, and they’ll get a crowdsource judgment of, are they right or are they wrong?\u003c/p>\n\u003cp>DANIEL: For instance, am I wrong for leaving my trash in a park that had no trash bins in it? The crowdsource consensus was yes, but the AI models often took a different approach.\u003c/p>\n\u003cp>CHENG: They gave responses like, no, you’re not in the wrong. It’s perfectly reasonable that you, like, left the trash on the branches of a tree because there was no trash bins available. You did the best you could.\u003c/p>\n\u003cp>DANIEL: In threads where the human community had decided someone was wrong, the AI affirmed the behavior roughly half the time. Cheng then wanted to examine the impact of these affirmations. That meant, in part, inviting 800 people to interact with either an affirming AI or a non-affirming AI about an actual conflict from their lives where they may or may not have been in the wrong.\u003c/p>\n\u003cp>CHENG: Something where you were talking to your ex or your friend, and that led to mixed feelings or misunderstandings.\u003c/p>\n\u003cp>DANIEL: Cheng and her colleagues then asked the participants to reflect on how they felt. Those who had interacted with the affirming AI…\u003c/p>\n\u003cp>CHENG: Became more self-centered. They became more convinced that they were right.\u003c/p>\n\u003cp>DANIEL: Specifically, 25% more convinced, compared to those interacting with the non-affirming AI. And they were also 10% less willing to apologize, fix the situation or change their behavior. Cheng says such relentless affirmation can negatively impact someone’s attitudes and judgments.\u003c/p>\n\u003cp>CHENG: People might be worse at handling their interpersonal relationships. They might be less willing to navigate conflict.\u003c/p>\n\u003cp>DANIEL: The research is published in the journal Science.\u003c/p>\n\u003cp>ISHTIAQUE AHMED: This is a very, you know, like a slow and invisible dark sides of AI.\u003c/p>\n\u003cp>DANIEL: Ishtiaque Ahmed is a computer scientist at the University of Toronto, who wasn’t involved in the study.\u003c/p>\n\u003cp>AHMED: When you constantly validate whatever someone is saying, they do not question their own decisions.\u003c/p>\n\u003cp>DANIEL: Ahmed says that when a person’s self-criticism becomes eroded, it can lead to bad choices and even emotional or physical harm.\u003c/p>\n\u003cp>AHMED: On the surface, it looks nice. AI is being nice to you, but they’re getting addicted to AIs because it keeps validating them.\u003c/p>\n\u003cp>DANIEL: As for what’s to be done, Myra Cheng says that companies and policymakers should work together to fix the problem, as these AIs are built deliberately by people and can be modified to be less affirming.\u003c/p>\n\u003cp>CHENG: But at the same time, I think maybe the biggest recommendation is to not use AI to substitute conversations that you would be having with other people.\u003c/p>\n\u003cp>DANIEL: Especially the tough conversations. For NPR News, I’m Ari Daniel.\u003c/p>\n\u003cp>(SOUNDBITE OF MUSIC)\u003c/p>\n\u003c/div>\n\n\u003c/div>\u003c/p>",
"attributes": {
"named": {},
"numeric": []
}
}
],
"link": "/mindshift/66289/do-you-like-ai-because-ai-likes-you-how-ai-flattery-crosses-signals",
"authors": [
"byline_mindshift_66289"
],
"programs": [
"mindshift_21847"
],
"categories": [
"mindshift_21504"
],
"tags": [
"mindshift_22000",
"mindshift_1023",
"mindshift_21511"
],
"featImg": "mindshift_66290",
"label": "mindshift_21847"
},
"mindshift_66237": {
"type": "posts",
"id": "mindshift_66237",
"meta": {
"index": "posts_1716263798",
"site": "mindshift",
"id": "66237",
"score": null,
"sort": [
1775469630000
]
},
"guestAuthors": [],
"slug": "the-quest-to-build-a-better-ai-tutor",
"title": "The Quest to Build a Better AI Tutor",
"publishDate": 1775469630,
"format": "standard",
"headTitle": "The Quest to Build a Better AI Tutor | KQED",
"labelTerm": {
"term": 21847,
"site": "mindshift"
},
"content": "\u003cp>It’s easy to get swept up in the hype about artificial intelligence tutors. But the evidence so far suggests caution.\u003c/p>\n\u003cp>Some studies have found that chatbot tutors can \u003ca href=\"https://www.pnas.org/doi/10.1073/pnas.2422633122\">backfire\u003c/a> because students \u003ca href=\"https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5604932\">lean on them\u003c/a> too heavily, get spoonfed solutions and fail to absorb the material. Even when AI tutors are designed not to give away answers, they haven’t consistently produced better results than learning the old-fashioned way without AI.\u003c/p>\n\u003cp>Still, researchers who have produced these skeptical studies haven’t given up hope. Some are still experimenting, trying to build better AI tutors.\u003c/p>\n\u003cp>One promising idea has less to do with how an AI tutor explains concepts and more with what it asks students to practice next.\u003c/p>\n\u003cp>A team at the University of Pennsylvania, which included some AI skeptics, recently tested this approach in a \u003ca href=\"https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6423358\">study\u003c/a> of close to 800 Taiwanese high school students learning Python programming. All the students used the same AI tutor, which was designed not to give away answers.\u003c/p>\n\u003cp>[ad fullwidth]\u003c/p>\n\u003cp>But there was one key difference. Half the students were randomly assigned to a fixed sequence of practice problems, progressing from easy to hard. The other half received a personalized sequence with the AI tutor continuously adjusting the difficulty of each problem based on how the student was performing and interacting with the chatbot.\u003c/p>\n\u003cp>The idea is based on what educators call the “zone of proximal development.” When problems are too easy, students get bored. When they’re too hard, students get frustrated. The goal is to keep students in a sweet spot: challenged, but not overwhelmed.\u003c/p>\n\u003cp>The researchers found that students in the personalized group did better on a final exam than students in the fixed problem group. The difference was characterized as the equivalent of 6 to 9 months of additional schooling, an eye-catching claim for an after-school online course that lasted only five months. The AI tutor’s inventor, Angel Chung, a doctoral student at the Wharton School, acknowledged that her conversion of statistical units was “not a perfect estimate.” (A \u003ca href=\"https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6423358\">draft paper\u003c/a> about the experiment was posted online in March 2026, but has not yet been published in a peer-reviewed journal.)\u003c/p>\n\u003cp>Still, this is early evidence that small tweaks — in this case, calibrating the difficulty of the practice problems to the student — can make a difference.\u003c/p>\n\u003cp>Chung said that ChatGPT’s responses may already feel very personal because they are directly responding to a student’s unique questions. But that level of personalization isn’t enough. “Students usually don’t know what they don’t know,” said Chung. “The student doesn’t have the ability to ask the right questions to get the best tutoring.”\u003c/p>\n\u003cp>To address this, Chung’s team combined a large language model with a separate machine-learning algorithm that analyzes how students interact with the online course platform — how they answer the practice questions, how many times they revise or edit their coding, and the quality of their conversations with the chatbot — and uses that information to decide which problem to serve up next.\u003c/p>\n\u003ch2>\u003cstrong>How different students interact with the chatbot tutor\u003c/strong>\u003c/h2>\n\u003cfigure id=\"attachment_66238\" class=\"wp-caption alignnone\" style=\"max-width: 780px\">\u003cimg loading=\"lazy\" decoding=\"async\" class=\"size-full wp-image-66238\" src=\"https://cdn.kqed.org/wp-content/uploads/sites/23/2026/04/Barshay-AI-Tutor-1.png\" alt=\"List of chatbot prompts\" width=\"780\" height=\"418\" srcset=\"https://cdn.kqed.org/wp-content/uploads/sites/23/2026/04/Barshay-AI-Tutor-1.png 780w, https://cdn.kqed.org/wp-content/uploads/sites/23/2026/04/Barshay-AI-Tutor-1-160x86.png 160w, https://cdn.kqed.org/wp-content/uploads/sites/23/2026/04/Barshay-AI-Tutor-1-768x412.png 768w\" sizes=\"auto, (max-width: 780px) 100vw, 780px\">\u003cfigcaption class=\"wp-caption-text\">Source: Chung et al, Effective Personalized AI Tutors via LLM-Guided Reinforcement Learning, March 2026\u003c/figcaption>\u003c/figure>\n\u003cp>In other words, personalization isn’t just about tailoring explanations. It’s about tailoring the learning path itself.\u003c/p>\n\u003cp>That idea isn’t new.\u003c/p>\n\u003cp>Long before generative AI tools like ChatGPT were invented, education researchers developed “intelligent tutoring systems” that tried to do something similar: estimate what a student knew and deliver the right next problem. These earlier systems couldn’t produce natural conversations, but they could provide hints and instant feedback. Rigorous studies found that well-designed versions helped students learn significantly more.\u003c/p>\n\u003cp>Their Achilles’ heel was engagement. Many students simply didn’t want to use them.\u003c/p>\n\u003cp>Today’s AI tools could help address that problem. Students might feel more interested in a chatbot that converses with them in an almost human way.\u003c/p>\n\u003cp>In the University of Pennsylvania study, students in the personalized group spent more time practicing, about three additional minutes per problem, adding up to about an hour per module in the Python course, compared with half as much time (a half hour or less) for the comparison students. The researchers think these students did better because they were more engaged in their practice work.\u003c/p>\n\u003cp>Students’ previous knowledge of a subject affected how well the personalized sequencing worked. Students who were new to Python gained more than those who already had Python experience, who did just as well with the fixed sequence of practice problems. Students from less elite high schools also appeared to benefit more.\u003c/p>\n\u003ch2>\u003cstrong>How students’ background affected results\u003c/strong>\u003c/h2>\n\u003cfigure id=\"attachment_66239\" class=\"wp-caption alignnone\" style=\"max-width: 780px\">\u003cimg loading=\"lazy\" decoding=\"async\" class=\"size-full wp-image-66239\" src=\"https://cdn.kqed.org/wp-content/uploads/sites/23/2026/04/Barshay-AI-Tutor-2.png\" alt=\"Chart showing skill vs. prior experience\" width=\"780\" height=\"500\" srcset=\"https://cdn.kqed.org/wp-content/uploads/sites/23/2026/04/Barshay-AI-Tutor-2.png 780w, https://cdn.kqed.org/wp-content/uploads/sites/23/2026/04/Barshay-AI-Tutor-2-160x103.png 160w, https://cdn.kqed.org/wp-content/uploads/sites/23/2026/04/Barshay-AI-Tutor-2-768x492.png 768w\" sizes=\"auto, (max-width: 780px) 100vw, 780px\">\u003cfigcaption class=\"wp-caption-text\">All students had access to the same AI tutor. The treatment difference compares a personalized sequence of problems difficulty rather versus a fixed sequence, from easy to hard. Source: Chung et al, Effective Personalized AI Tutors via LLM-Guided Reinforcement Learning, March 2026\u003c/figcaption>\u003c/figure>\n\u003cp>All the Taiwanese students in this study volunteered for an optional computer programming course that could strengthen their college applications. Many were highly motivated, with highly educated parents, and many already had prior coding experience.\u003c/p>\n\u003cp>It’s not clear whether the chatbot would work as well with less motivated students who are behind at school and most in need of extra help.\u003c/p>\n\u003cp>One possible solution: fusing new and old.\u003c/p>\n\u003cp>Ken Koedinger, a professor at Carnegie Mellon University and a pioneer of intelligent tutoring systems, is experimenting with using \u003ca href=\"https://dl.acm.org/doi/abs/10.1145/3698205.3733948\">new AI models to alert remote human tutors\u003c/a> who can motivate struggling students who are drifting off. “We are having more success,” said Koedinger.\u003c/p>\n\u003cp>Humans aren’t obsolete — yet.\u003c/p>\n\u003cp>\u003c/p>\n\u003cp>\u003cem>This story about \u003c/em>\u003ca href=\"https://hechingerreport.org/proof-points-ai-tutor-python/\">\u003cem>AI tutors\u003c/em>\u003c/a>\u003cem> was produced by \u003c/em>\u003ca href=\"https://hechingerreport.org/special-reports/higher-education/\">The Hechinger Report\u003c/a>\u003cem>, a nonprofit, independent news organization that covers education. Sign up for \u003c/em>\u003ca href=\"https://hechingerreport.org/proofpoints/\">\u003cem>Proof Points\u003c/em>\u003c/a>\u003cem> and other \u003c/em>\u003ca href=\"https://hechingerreport.org/newsletters/\">\u003cem>Hechinger newsletters\u003c/em>\u003c/a>\u003cem>.\u003c/em>\u003c/p>\n\n",
"blocks": [],
"excerpt": "Researchers make progress with an older ed tech idea: personalized practice.",
"status": "publish",
"parent": 0,
"modified": 1775458566,
"stats": {
"hasAudio": false,
"hasVideo": false,
"hasChartOrMap": false,
"iframeSrcs": [],
"hasGoogleForm": false,
"hasGallery": false,
"hasHearkenModule": false,
"hasPolis": false,
"paragraphCount": 26,
"wordCount": 1007
},
"headData": {
"title": "The Quest to Build a Better AI Tutor | KQED",
"description": "Researchers make progress with an older ed tech idea: personalized practice.",
"ogTitle": "",
"ogDescription": "",
"ogImgId": "",
"twTitle": "",
"twDescription": "",
"twImgId": "",
"schema": {
"@context": "https://schema.org",
"@type": "Article",
"headline": "The Quest to Build a Better AI Tutor",
"datePublished": "2026-04-06T03:00:30-07:00",
"dateModified": "2026-04-05T23:56:06-07:00",
"image": "https://cdn.kqed.org/wp-content/uploads/2020/02/KQED-OG-Image@1x.png"
}
},
"primaryCategory": {
"termId": 21504,
"slug": "education-research",
"name": "Education research"
},
"sticky": false,
"nprByline": "Jill Barshay, \u003ca href=\"https://hechingerreport.org/\">The Hechinger Report\u003c/a>",
"templateType": "standard",
"featuredImageType": "standard",
"excludeFromSiteSearch": "Include",
"showOnAuthorArchivePages": "No",
"articleAge": "0",
"path": "/mindshift/66237/the-quest-to-build-a-better-ai-tutor",
"audioTrackLength": null,
"parsedContent": [
{
"type": "contentString",
"content": "\u003cdiv class=\"post-body\">\u003cp>\u003cp>It’s easy to get swept up in the hype about artificial intelligence tutors. But the evidence so far suggests caution.\u003c/p>\n\u003cp>Some studies have found that chatbot tutors can \u003ca href=\"https://www.pnas.org/doi/10.1073/pnas.2422633122\">backfire\u003c/a> because students \u003ca href=\"https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5604932\">lean on them\u003c/a> too heavily, get spoonfed solutions and fail to absorb the material. Even when AI tutors are designed not to give away answers, they haven’t consistently produced better results than learning the old-fashioned way without AI.\u003c/p>\n\u003cp>Still, researchers who have produced these skeptical studies haven’t given up hope. Some are still experimenting, trying to build better AI tutors.\u003c/p>\n\u003cp>One promising idea has less to do with how an AI tutor explains concepts and more with what it asks students to practice next.\u003c/p>\n\u003cp>A team at the University of Pennsylvania, which included some AI skeptics, recently tested this approach in a \u003ca href=\"https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6423358\">study\u003c/a> of close to 800 Taiwanese high school students learning Python programming. All the students used the same AI tutor, which was designed not to give away answers.\u003c/p>\n\u003cp>\u003c/p>\u003c/div>",
"attributes": {
"named": {},
"numeric": []
}
},
{
"type": "component",
"content": "",
"name": "ad",
"attributes": {
"named": {
"label": "fullwidth"
},
"numeric": [
"fullwidth"
]
}
},
{
"type": "contentString",
"content": "\u003cdiv class=\"post-body\">\u003cp>\u003c/p>\n\u003cp>But there was one key difference. Half the students were randomly assigned to a fixed sequence of practice problems, progressing from easy to hard. The other half received a personalized sequence with the AI tutor continuously adjusting the difficulty of each problem based on how the student was performing and interacting with the chatbot.\u003c/p>\n\u003cp>The idea is based on what educators call the “zone of proximal development.” When problems are too easy, students get bored. When they’re too hard, students get frustrated. The goal is to keep students in a sweet spot: challenged, but not overwhelmed.\u003c/p>\n\u003cp>The researchers found that students in the personalized group did better on a final exam than students in the fixed problem group. The difference was characterized as the equivalent of 6 to 9 months of additional schooling, an eye-catching claim for an after-school online course that lasted only five months. The AI tutor’s inventor, Angel Chung, a doctoral student at the Wharton School, acknowledged that her conversion of statistical units was “not a perfect estimate.” (A \u003ca href=\"https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6423358\">draft paper\u003c/a> about the experiment was posted online in March 2026, but has not yet been published in a peer-reviewed journal.)\u003c/p>\n\u003cp>Still, this is early evidence that small tweaks — in this case, calibrating the difficulty of the practice problems to the student — can make a difference.\u003c/p>\n\u003cp>Chung said that ChatGPT’s responses may already feel very personal because they are directly responding to a student’s unique questions. But that level of personalization isn’t enough. “Students usually don’t know what they don’t know,” said Chung. “The student doesn’t have the ability to ask the right questions to get the best tutoring.”\u003c/p>\n\u003cp>To address this, Chung’s team combined a large language model with a separate machine-learning algorithm that analyzes how students interact with the online course platform — how they answer the practice questions, how many times they revise or edit their coding, and the quality of their conversations with the chatbot — and uses that information to decide which problem to serve up next.\u003c/p>\n\u003ch2>\u003cstrong>How different students interact with the chatbot tutor\u003c/strong>\u003c/h2>\n\u003cfigure id=\"attachment_66238\" class=\"wp-caption alignnone\" style=\"max-width: 780px\">\u003cimg loading=\"lazy\" decoding=\"async\" class=\"size-full wp-image-66238\" src=\"https://cdn.kqed.org/wp-content/uploads/sites/23/2026/04/Barshay-AI-Tutor-1.png\" alt=\"List of chatbot prompts\" width=\"780\" height=\"418\" srcset=\"https://cdn.kqed.org/wp-content/uploads/sites/23/2026/04/Barshay-AI-Tutor-1.png 780w, https://cdn.kqed.org/wp-content/uploads/sites/23/2026/04/Barshay-AI-Tutor-1-160x86.png 160w, https://cdn.kqed.org/wp-content/uploads/sites/23/2026/04/Barshay-AI-Tutor-1-768x412.png 768w\" sizes=\"auto, (max-width: 780px) 100vw, 780px\">\u003cfigcaption class=\"wp-caption-text\">Source: Chung et al, Effective Personalized AI Tutors via LLM-Guided Reinforcement Learning, March 2026\u003c/figcaption>\u003c/figure>\n\u003cp>In other words, personalization isn’t just about tailoring explanations. It’s about tailoring the learning path itself.\u003c/p>\n\u003cp>That idea isn’t new.\u003c/p>\n\u003cp>Long before generative AI tools like ChatGPT were invented, education researchers developed “intelligent tutoring systems” that tried to do something similar: estimate what a student knew and deliver the right next problem. These earlier systems couldn’t produce natural conversations, but they could provide hints and instant feedback. Rigorous studies found that well-designed versions helped students learn significantly more.\u003c/p>\n\u003cp>Their Achilles’ heel was engagement. Many students simply didn’t want to use them.\u003c/p>\n\u003cp>Today’s AI tools could help address that problem. Students might feel more interested in a chatbot that converses with them in an almost human way.\u003c/p>\n\u003cp>In the University of Pennsylvania study, students in the personalized group spent more time practicing, about three additional minutes per problem, adding up to about an hour per module in the Python course, compared with half as much time (a half hour or less) for the comparison students. The researchers think these students did better because they were more engaged in their practice work.\u003c/p>\n\u003cp>Students’ previous knowledge of a subject affected how well the personalized sequencing worked. Students who were new to Python gained more than those who already had Python experience, who did just as well with the fixed sequence of practice problems. Students from less elite high schools also appeared to benefit more.\u003c/p>\n\u003ch2>\u003cstrong>How students’ background affected results\u003c/strong>\u003c/h2>\n\u003cfigure id=\"attachment_66239\" class=\"wp-caption alignnone\" style=\"max-width: 780px\">\u003cimg loading=\"lazy\" decoding=\"async\" class=\"size-full wp-image-66239\" src=\"https://cdn.kqed.org/wp-content/uploads/sites/23/2026/04/Barshay-AI-Tutor-2.png\" alt=\"Chart showing skill vs. prior experience\" width=\"780\" height=\"500\" srcset=\"https://cdn.kqed.org/wp-content/uploads/sites/23/2026/04/Barshay-AI-Tutor-2.png 780w, https://cdn.kqed.org/wp-content/uploads/sites/23/2026/04/Barshay-AI-Tutor-2-160x103.png 160w, https://cdn.kqed.org/wp-content/uploads/sites/23/2026/04/Barshay-AI-Tutor-2-768x492.png 768w\" sizes=\"auto, (max-width: 780px) 100vw, 780px\">\u003cfigcaption class=\"wp-caption-text\">All students had access to the same AI tutor. The treatment difference compares a personalized sequence of problems difficulty rather versus a fixed sequence, from easy to hard. Source: Chung et al, Effective Personalized AI Tutors via LLM-Guided Reinforcement Learning, March 2026\u003c/figcaption>\u003c/figure>\n\u003cp>All the Taiwanese students in this study volunteered for an optional computer programming course that could strengthen their college applications. Many were highly motivated, with highly educated parents, and many already had prior coding experience.\u003c/p>\n\u003cp>It’s not clear whether the chatbot would work as well with less motivated students who are behind at school and most in need of extra help.\u003c/p>\n\u003cp>One possible solution: fusing new and old.\u003c/p>\n\u003cp>Ken Koedinger, a professor at Carnegie Mellon University and a pioneer of intelligent tutoring systems, is experimenting with using \u003ca href=\"https://dl.acm.org/doi/abs/10.1145/3698205.3733948\">new AI models to alert remote human tutors\u003c/a> who can motivate struggling students who are drifting off. “We are having more success,” said Koedinger.\u003c/p>\n\u003cp>Humans aren’t obsolete — yet.\u003c/p>\n\u003cp>\u003c/p>\n\u003cp>\u003cem>This story about \u003c/em>\u003ca href=\"https://hechingerreport.org/proof-points-ai-tutor-python/\">\u003cem>AI tutors\u003c/em>\u003c/a>\u003cem> was produced by \u003c/em>\u003ca href=\"https://hechingerreport.org/special-reports/higher-education/\">The Hechinger Report\u003c/a>\u003cem>, a nonprofit, independent news organization that covers education. Sign up for \u003c/em>\u003ca href=\"https://hechingerreport.org/proofpoints/\">\u003cem>Proof Points\u003c/em>\u003c/a>\u003cem> and other \u003c/em>\u003ca href=\"https://hechingerreport.org/newsletters/\">\u003cem>Hechinger newsletters\u003c/em>\u003c/a>\u003cem>.\u003c/em>\u003c/p>\n\n\u003c/div>\u003c/p>",
"attributes": {
"named": {},
"numeric": []
}
}
],
"link": "/mindshift/66237/the-quest-to-build-a-better-ai-tutor",
"authors": [
"byline_mindshift_66237"
],
"programs": [
"mindshift_21847"
],
"categories": [
"mindshift_21504"
],
"tags": [
"mindshift_1023",
"mindshift_918",
"mindshift_21413",
"mindshift_21102"
],
"featImg": "mindshift_66240",
"label": "mindshift_21847"
},
"mindshift_66217": {
"type": "posts",
"id": "mindshift_66217",
"meta": {
"index": "posts_1716263798",
"site": "mindshift",
"id": "66217",
"score": null,
"sort": [
1774260006000
]
},
"guestAuthors": [],
"slug": "the-ai-hivemind-why-so-many-student-essays-sound-alike",
"title": "The AI ‘Hivemind’: Why So Many Student Essays Sound Alike",
"publishDate": 1774260006,
"format": "standard",
"headTitle": "The AI ‘Hivemind’: Why So Many Student Essays Sound Alike | KQED",
"labelTerm": {
"term": 21847,
"site": "mindshift"
},
"content": "\u003cp>Bruce Maxwell, professor of computer science at Northeastern University, was grading exams for his online master’s course in computer vision, a subfield in artificial intelligence that deals with images, when he first noticed that something felt … off.\u003c/p>\n\u003cp>“I’d see the same phrases, the same commas, even the same word choices. I would say, ‘Man, I’ve read that before.’ And I’d go look for it,” said Maxwell. “The paragraphs weren’t identical, but they were so similar.”\u003c/p>\n\u003cp>Although the course was in 2024, Maxwell, who teaches at Northeastern’s Seattle campus, recalls that his students’ essays sounded “like textbooks written in the 1980s and ’90s,” perhaps reflecting the sources used to train AI. The students were scattered around the country and Maxwell was pretty sure they hadn’t collaborated.\u003c/p>\n\u003cp>Maxwell shared his observation with a former student, Liwei Jiang, who is now a Ph.D. student in computer science and engineering at the University of Washington. Jiang decided to test her former professor’s hunch about AI scientifically and collaborated with other researchers at UW, the Allen Institute for Artificial Intelligence, Stanford and Carnegie Mellon universities to analyze the output from more than 70 different large language models around the globe, including ChatGPT, Claude, Gemini, DeepSeek, Qwen and Llama.\u003c/p>\n\u003cp>The team asked each the same open-ended questions, which were intended to spark creativity or brainstorm new ideas: “Compose a short poem about the feeling of watching a sunset;” “I am a graduate student in Marxist theory, and I want to write a thesis on Gorz. Can you help me think of some new ideas?” and “Write a 30-word essay on global warming.” (The researchers pulled the questions from a corpus of real ChatGPT questions that users had consented to make public in exchange for free access to a more advanced model.) The researchers posed 100 of these questions to all 70 models and had each model answer them 50 times.\u003c/p>\n\u003cp>[ad fullwidth]\u003c/p>\n\u003cp>The answers were often indistinguishable across different models by different companies that have different architectures and use different training data. The metaphors, imagery, word choices, sentence structures — even punctuation — often converged. Jiang’s team called this phenomenon “inter-model homogeneity” and quantified the overlaps and similarities. To drive the point home, Jiang titled her paper, the “\u003ca href=\"https://arxiv.org/pdf/2510.22954\">Artificial Hivemind.\u003c/a>” The study won the best paper award at the annual conference on Neural Information Processing Systems in December 2025, one of the premier gatherings for AI research.\u003c/p>\n\u003cp>To increase AI creativity, Jiang jacked up a parameter, called “temperature,” to maximize the randomness of each large language model. That didn’t help. For example, when she asked an AI model called Claude 3.5 Sonnet to “write a short story about a colorful toad who goes on an adventure in 50 words,” it kept naming the toad Ziggy or Pip, and oddly, a hungry hawk and mushrooms kept appearing.\u003c/p>\n\u003cfigure id=\"attachment_66219\" class=\"wp-caption alignnone\" style=\"max-width: 2734px\">\u003cimg loading=\"lazy\" decoding=\"async\" class=\"size-full wp-image-66219\" src=\"https://cdn.kqed.org/wp-content/uploads/sites/23/2026/03/Colorful-Toad.png\" alt=\"\" width=\"2734\" height=\"1498\" srcset=\"https://cdn.kqed.org/wp-content/uploads/sites/23/2026/03/Colorful-Toad.png 2734w, https://cdn.kqed.org/wp-content/uploads/sites/23/2026/03/Colorful-Toad-2000x1096.png 2000w, https://cdn.kqed.org/wp-content/uploads/sites/23/2026/03/Colorful-Toad-160x88.png 160w, https://cdn.kqed.org/wp-content/uploads/sites/23/2026/03/Colorful-Toad-768x421.png 768w, https://cdn.kqed.org/wp-content/uploads/sites/23/2026/03/Colorful-Toad-1536x842.png 1536w, https://cdn.kqed.org/wp-content/uploads/sites/23/2026/03/Colorful-Toad-2048x1122.png 2048w\" sizes=\"auto, (max-width: 2734px) 100vw, 2734px\">\u003cfigcaption class=\"wp-caption-text\">Presentation slide courtesy of Liwei Jiang, the AI study’s lead author.\u003c/figcaption>\u003c/figure>\n\u003cp>Different models also churn out comically similar responses. When asked to come up with a metaphor for time, the overwhelming answer from all the models was the same: a river. A few said a weaver. One outlier suggested a sculptor. Several of the models were developed in China, and yet, they were producing similar answers to those made in America.\u003c/p>\n\u003cp>\u003cstrong>Example of similar output from ChatGPT and DeepSeek\u003c/strong>\u003c/p>\n\u003cfigure id=\"attachment_66218\" class=\"wp-caption alignnone\" style=\"max-width: 2692px\">\u003cimg loading=\"lazy\" decoding=\"async\" class=\"size-full wp-image-66218\" src=\"https://cdn.kqed.org/wp-content/uploads/sites/23/2026/03/Intermodel-homogeneity-v2.png\" alt=\"\" width=\"2692\" height=\"1566\" srcset=\"https://cdn.kqed.org/wp-content/uploads/sites/23/2026/03/Intermodel-homogeneity-v2.png 2692w, https://cdn.kqed.org/wp-content/uploads/sites/23/2026/03/Intermodel-homogeneity-v2-2000x1163.png 2000w, https://cdn.kqed.org/wp-content/uploads/sites/23/2026/03/Intermodel-homogeneity-v2-160x93.png 160w, https://cdn.kqed.org/wp-content/uploads/sites/23/2026/03/Intermodel-homogeneity-v2-768x447.png 768w, https://cdn.kqed.org/wp-content/uploads/sites/23/2026/03/Intermodel-homogeneity-v2-1536x894.png 1536w, https://cdn.kqed.org/wp-content/uploads/sites/23/2026/03/Intermodel-homogeneity-v2-2048x1191.png 2048w\" sizes=\"auto, (max-width: 2692px) 100vw, 2692px\">\u003cfigcaption class=\"wp-caption-text\">Presentation slide courtesy of Liwei Jiang, the AI study’s lead author.\u003c/figcaption>\u003c/figure>\n\u003cp>The explanation lies in chatbot design. AI chatbots are trained to review possible answers to make sure the output is reasonable, appropriate and helpful. This refinement step, sometimes called “alignment,” is intended to ensure that the answers align to or match what a human would prefer. And it’s this alignment step, according to Jiang, that is creating the homogeneity. The process favors safe, consensus-based responses and penalizes risky, unconventional ones. Originality gets stripped away.\u003c/p>\n\u003cp>Jiang’s advice for students is to push themselves to go beyond what the AI model spits out. “The model is actually generating some good ideas, but you need to go the extra mile to be more creative than that,” said Jiang.\u003c/p>\n\u003cp>For Jiang’s former professor Maxwell, the study confirmed what he had suspected. And even before Jiang’s paper came out, he changed how he teaches. He no longer relies on online exams. Instead, he now asks students to learn a concept and present it to other students or create a video tutorial.\u003c/p>\n\u003cp>Outwitting the AI hive mind requires some post-modern creativity.\u003c/p>\n\u003cp>\u003c/p>\n\u003cp>\u003cem>This story about \u003c/em>\u003ca href=\"https://hechingerreport.org/proof-points-ai-similarity/\">\u003cem>similar AI answers\u003c/em>\u003c/a>\u003cem> was produced by \u003c/em>\u003ca href=\"https://hechingerreport.org/special-reports/higher-education/\">The Hechinger Report\u003c/a>\u003cem>, a nonprofit, independent news organization that covers education. Sign up for \u003c/em>\u003ca href=\"https://hechingerreport.org/proofpoints/\">\u003cem>Proof Points\u003c/em>\u003c/a>\u003cem> and other \u003c/em>\u003ca href=\"https://hechingerreport.org/newsletters/\">\u003cem>Hechinger newsletters\u003c/em>\u003c/a>\u003cem>.\u003c/em>\u003c/p>\n\n",
"blocks": [],
"excerpt": "A study of more than 70 large language models found similar answers to brainstorming and creative writing prompts; originality gets stripped away and the process favors safe, consensus-based responses. ",
"status": "publish",
"parent": 0,
"modified": 1774300003,
"stats": {
"hasAudio": false,
"hasVideo": false,
"hasChartOrMap": false,
"iframeSrcs": [],
"hasGoogleForm": false,
"hasGallery": false,
"hasHearkenModule": false,
"hasPolis": false,
"paragraphCount": 16,
"wordCount": 832
},
"headData": {
"title": "The AI ‘Hivemind’: Why So Many Student Essays Sound Alike | KQED",
"description": "A study of more than 70 large language models found similar answers to brainstorming and creative writing prompts; originality gets stripped away and the process favors safe, consensus-based responses. ",
"ogTitle": "",
"ogDescription": "",
"ogImgId": "",
"twTitle": "",
"twDescription": "",
"twImgId": "",
"schema": {
"@context": "https://schema.org",
"@type": "Article",
"headline": "The AI ‘Hivemind’: Why So Many Student Essays Sound Alike",
"datePublished": "2026-03-23T03:00:06-07:00",
"dateModified": "2026-03-23T14:06:43-07:00",
"image": "https://cdn.kqed.org/wp-content/uploads/2020/02/KQED-OG-Image@1x.png"
}
},
"primaryCategory": {
"termId": 21504,
"slug": "education-research",
"name": "Education research"
},
"sticky": false,
"nprByline": "Jill Barshay, \u003ca href=\"https://hechingerreport.org/\">The Hechinger Report\u003c/a>",
"templateType": "standard",
"featuredImageType": "standard",
"excludeFromSiteSearch": "Include",
"showOnAuthorArchivePages": "No",
"articleAge": "0",
"path": "/mindshift/66217/the-ai-hivemind-why-so-many-student-essays-sound-alike",
"audioTrackLength": null,
"parsedContent": [
{
"type": "contentString",
"content": "\u003cdiv class=\"post-body\">\u003cp>\u003cp>Bruce Maxwell, professor of computer science at Northeastern University, was grading exams for his online master’s course in computer vision, a subfield in artificial intelligence that deals with images, when he first noticed that something felt … off.\u003c/p>\n\u003cp>“I’d see the same phrases, the same commas, even the same word choices. I would say, ‘Man, I’ve read that before.’ And I’d go look for it,” said Maxwell. “The paragraphs weren’t identical, but they were so similar.”\u003c/p>\n\u003cp>Although the course was in 2024, Maxwell, who teaches at Northeastern’s Seattle campus, recalls that his students’ essays sounded “like textbooks written in the 1980s and ’90s,” perhaps reflecting the sources used to train AI. The students were scattered around the country and Maxwell was pretty sure they hadn’t collaborated.\u003c/p>\n\u003cp>Maxwell shared his observation with a former student, Liwei Jiang, who is now a Ph.D. student in computer science and engineering at the University of Washington. Jiang decided to test her former professor’s hunch about AI scientifically and collaborated with other researchers at UW, the Allen Institute for Artificial Intelligence, Stanford and Carnegie Mellon universities to analyze the output from more than 70 different large language models around the globe, including ChatGPT, Claude, Gemini, DeepSeek, Qwen and Llama.\u003c/p>\n\u003cp>The team asked each the same open-ended questions, which were intended to spark creativity or brainstorm new ideas: “Compose a short poem about the feeling of watching a sunset;” “I am a graduate student in Marxist theory, and I want to write a thesis on Gorz. Can you help me think of some new ideas?” and “Write a 30-word essay on global warming.” (The researchers pulled the questions from a corpus of real ChatGPT questions that users had consented to make public in exchange for free access to a more advanced model.) The researchers posed 100 of these questions to all 70 models and had each model answer them 50 times.\u003c/p>\n\u003cp>\u003c/p>\u003c/div>",
"attributes": {
"named": {},
"numeric": []
}
},
{
"type": "component",
"content": "",
"name": "ad",
"attributes": {
"named": {
"label": "fullwidth"
},
"numeric": [
"fullwidth"
]
}
},
{
"type": "contentString",
"content": "\u003cdiv class=\"post-body\">\u003cp>\u003c/p>\n\u003cp>The answers were often indistinguishable across different models by different companies that have different architectures and use different training data. The metaphors, imagery, word choices, sentence structures — even punctuation — often converged. Jiang’s team called this phenomenon “inter-model homogeneity” and quantified the overlaps and similarities. To drive the point home, Jiang titled her paper, the “\u003ca href=\"https://arxiv.org/pdf/2510.22954\">Artificial Hivemind.\u003c/a>” The study won the best paper award at the annual conference on Neural Information Processing Systems in December 2025, one of the premier gatherings for AI research.\u003c/p>\n\u003cp>To increase AI creativity, Jiang jacked up a parameter, called “temperature,” to maximize the randomness of each large language model. That didn’t help. For example, when she asked an AI model called Claude 3.5 Sonnet to “write a short story about a colorful toad who goes on an adventure in 50 words,” it kept naming the toad Ziggy or Pip, and oddly, a hungry hawk and mushrooms kept appearing.\u003c/p>\n\u003cfigure id=\"attachment_66219\" class=\"wp-caption alignnone\" style=\"max-width: 2734px\">\u003cimg loading=\"lazy\" decoding=\"async\" class=\"size-full wp-image-66219\" src=\"https://cdn.kqed.org/wp-content/uploads/sites/23/2026/03/Colorful-Toad.png\" alt=\"\" width=\"2734\" height=\"1498\" srcset=\"https://cdn.kqed.org/wp-content/uploads/sites/23/2026/03/Colorful-Toad.png 2734w, https://cdn.kqed.org/wp-content/uploads/sites/23/2026/03/Colorful-Toad-2000x1096.png 2000w, https://cdn.kqed.org/wp-content/uploads/sites/23/2026/03/Colorful-Toad-160x88.png 160w, https://cdn.kqed.org/wp-content/uploads/sites/23/2026/03/Colorful-Toad-768x421.png 768w, https://cdn.kqed.org/wp-content/uploads/sites/23/2026/03/Colorful-Toad-1536x842.png 1536w, https://cdn.kqed.org/wp-content/uploads/sites/23/2026/03/Colorful-Toad-2048x1122.png 2048w\" sizes=\"auto, (max-width: 2734px) 100vw, 2734px\">\u003cfigcaption class=\"wp-caption-text\">Presentation slide courtesy of Liwei Jiang, the AI study’s lead author.\u003c/figcaption>\u003c/figure>\n\u003cp>Different models also churn out comically similar responses. When asked to come up with a metaphor for time, the overwhelming answer from all the models was the same: a river. A few said a weaver. One outlier suggested a sculptor. Several of the models were developed in China, and yet, they were producing similar answers to those made in America.\u003c/p>\n\u003cp>\u003cstrong>Example of similar output from ChatGPT and DeepSeek\u003c/strong>\u003c/p>\n\u003cfigure id=\"attachment_66218\" class=\"wp-caption alignnone\" style=\"max-width: 2692px\">\u003cimg loading=\"lazy\" decoding=\"async\" class=\"size-full wp-image-66218\" src=\"https://cdn.kqed.org/wp-content/uploads/sites/23/2026/03/Intermodel-homogeneity-v2.png\" alt=\"\" width=\"2692\" height=\"1566\" srcset=\"https://cdn.kqed.org/wp-content/uploads/sites/23/2026/03/Intermodel-homogeneity-v2.png 2692w, https://cdn.kqed.org/wp-content/uploads/sites/23/2026/03/Intermodel-homogeneity-v2-2000x1163.png 2000w, https://cdn.kqed.org/wp-content/uploads/sites/23/2026/03/Intermodel-homogeneity-v2-160x93.png 160w, https://cdn.kqed.org/wp-content/uploads/sites/23/2026/03/Intermodel-homogeneity-v2-768x447.png 768w, https://cdn.kqed.org/wp-content/uploads/sites/23/2026/03/Intermodel-homogeneity-v2-1536x894.png 1536w, https://cdn.kqed.org/wp-content/uploads/sites/23/2026/03/Intermodel-homogeneity-v2-2048x1191.png 2048w\" sizes=\"auto, (max-width: 2692px) 100vw, 2692px\">\u003cfigcaption class=\"wp-caption-text\">Presentation slide courtesy of Liwei Jiang, the AI study’s lead author.\u003c/figcaption>\u003c/figure>\n\u003cp>The explanation lies in chatbot design. AI chatbots are trained to review possible answers to make sure the output is reasonable, appropriate and helpful. This refinement step, sometimes called “alignment,” is intended to ensure that the answers align to or match what a human would prefer. And it’s this alignment step, according to Jiang, that is creating the homogeneity. The process favors safe, consensus-based responses and penalizes risky, unconventional ones. Originality gets stripped away.\u003c/p>\n\u003cp>Jiang’s advice for students is to push themselves to go beyond what the AI model spits out. “The model is actually generating some good ideas, but you need to go the extra mile to be more creative than that,” said Jiang.\u003c/p>\n\u003cp>For Jiang’s former professor Maxwell, the study confirmed what he had suspected. And even before Jiang’s paper came out, he changed how he teaches. He no longer relies on online exams. Instead, he now asks students to learn a concept and present it to other students or create a video tutorial.\u003c/p>\n\u003cp>Outwitting the AI hive mind requires some post-modern creativity.\u003c/p>\n\u003cp>\u003c/p>\n\u003cp>\u003cem>This story about \u003c/em>\u003ca href=\"https://hechingerreport.org/proof-points-ai-similarity/\">\u003cem>similar AI answers\u003c/em>\u003c/a>\u003cem> was produced by \u003c/em>\u003ca href=\"https://hechingerreport.org/special-reports/higher-education/\">The Hechinger Report\u003c/a>\u003cem>, a nonprofit, independent news organization that covers education. Sign up for \u003c/em>\u003ca href=\"https://hechingerreport.org/proofpoints/\">\u003cem>Proof Points\u003c/em>\u003c/a>\u003cem> and other \u003c/em>\u003ca href=\"https://hechingerreport.org/newsletters/\">\u003cem>Hechinger newsletters\u003c/em>\u003c/a>\u003cem>.\u003c/em>\u003c/p>\n\n\u003c/div>\u003c/p>",
"attributes": {
"named": {},
"numeric": []
}
}
],
"link": "/mindshift/66217/the-ai-hivemind-why-so-many-student-essays-sound-alike",
"authors": [
"byline_mindshift_66217"
],
"programs": [
"mindshift_21847"
],
"categories": [
"mindshift_21504"
],
"tags": [
"mindshift_21584",
"mindshift_1023",
"mindshift_862",
"mindshift_22002",
"mindshift_851"
],
"featImg": "mindshift_66220",
"label": "mindshift_21847"
}
},
"podcastsReducer": {
"isFetching": false,
"fetchFailed": false,
"hasFetched": false,
"podcasts": {}
},
"radioProgramsReducer": {
"isFetching": false,
"fetchFailed": false,
"hasFetched": false,
"radioPrograms": {}
},
"programsReducer": {
"all-things-considered": {
"id": "all-things-considered",
"title": "All Things Considered",
"info": "Every weekday, \u003cem>All Things Considered\u003c/em> hosts Robert Siegel, Audie Cornish, Ari Shapiro, and Kelly McEvers present the program's trademark mix of news, interviews, commentaries, reviews, and offbeat features. Michel Martin hosts on the weekends.",
"airtime": "MON-FRI 1pm-2pm, 4:30pm-6:30pm\u003cbr />SAT-SUN 5pm-6pm",
"imageSrc": "https://cdn.kqed.org/wp-content/uploads/2024/04/All-Things-Considered-Podcast-Tile-360x360-1.jpg",
"officialWebsiteLink": "https://www.npr.org/programs/all-things-considered/",
"meta": {
"site": "news",
"source": "npr"
},
"link": "/radio/program/all-things-considered"
},
"american-suburb-podcast": {
"id": "american-suburb-podcast",
"title": "American Suburb: The Podcast",
"tagline": "The flip side of gentrification, told through one town",
"info": "Gentrification is changing cities across America, forcing people from neighborhoods they have long called home. Call them the displaced. Now those priced out of the Bay Area are looking for a better life in an unlikely place. American Suburb follows this migration to one California town along the Delta, 45 miles from San Francisco. But is this once sleepy suburb ready for them?",
"imageSrc": "https://cdn.kqed.org/wp-content/uploads/2024/04/American-Suburb-Podcast-Tile-703x703-1.jpg",
"officialWebsiteLink": "/news/series/american-suburb-podcast",
"meta": {
"site": "news",
"source": "kqed",
"order": 19
},
"link": "/news/series/american-suburb-podcast/",
"subscribe": {
"npr": "https://rpb3r.app.goo.gl/RBrW",
"apple": "https://itunes.apple.com/WebObjects/MZStore.woa/wa/viewPodcast?mt=2&id=1287748328",
"tuneIn": "https://tunein.com/radio/American-Suburb-p1086805/",
"rss": "https://ww2.kqed.org/news/series/american-suburb-podcast/feed/podcast",
"google": "https://podcasts.google.com/feed/aHR0cHM6Ly9mZWVkcy5tZWdhcGhvbmUuZm0vS1FJTkMzMDExODgxNjA5"
}
},
"baycurious": {
"id": "baycurious",
"title": "Bay Curious",
"tagline": "Exploring the Bay Area, one question at a time",
"info": "KQED’s new podcast, Bay Curious, gets to the bottom of the mysteries — both profound and peculiar — that give the Bay Area its unique identity. And we’ll do it with your help! You ask the questions. You decide what Bay Curious investigates. And you join us on the journey to find the answers.",
"imageSrc": "https://cdn.kqed.org/wp-content/uploads/2024/04/Bay-Curious-Podcast-Tile-703x703-1.jpg",
"imageAlt": "KQED Bay Curious",
"officialWebsiteLink": "/news/series/baycurious",
"meta": {
"site": "news",
"source": "kqed",
"order": 3
},
"link": "/podcasts/baycurious",
"subscribe": {
"apple": "https://podcasts.apple.com/us/podcast/bay-curious/id1172473406",
"npr": "https://www.npr.org/podcasts/500557090/bay-curious",
"rss": "https://ww2.kqed.org/news/category/bay-curious-podcast/feed/podcast",
"amazon": "https://music.amazon.com/podcasts/9a90d476-aa04-455d-9a4c-0871ed6216d4/bay-curious",
"stitcher": "https://www.stitcher.com/podcast/kqed/bay-curious",
"spotify": "https://open.spotify.com/show/6O76IdmhixfijmhTZLIJ8k"
}
},
"bbc-world-service": {
"id": "bbc-world-service",
"title": "BBC World Service",
"info": "The day's top stories from BBC News compiled twice daily in the week, once at weekends.",
"airtime": "MON-FRI 9pm-10pm, TUE-FRI 1am-2am",
"imageSrc": "https://cdn.kqed.org/wp-content/uploads/2024/04/BBC-World-Service-Podcast-Tile-360x360-1.jpg",
"officialWebsiteLink": "https://www.bbc.co.uk/sounds/play/live:bbc_world_service",
"meta": {
"site": "news",
"source": "BBC World Service"
},
"link": "/radio/program/bbc-world-service",
"subscribe": {
"apple": "https://itunes.apple.com/us/podcast/global-news-podcast/id135067274?mt=2",
"tuneIn": "https://tunein.com/radio/BBC-World-Service-p455581/",
"rss": "https://podcasts.files.bbci.co.uk/p02nq0gn.rss"
}
},
"californiareport": {
"id": "californiareport",
"title": "The California Report",
"tagline": "California, day by day",
"info": "KQED’s statewide radio news program providing daily coverage of issues, trends and public policy decisions.",
"imageSrc": "https://cdn.kqed.org/wp-content/uploads/2024/04/The-California-Report-Podcast-Tile-703x703-1.jpg",
"imageAlt": "KQED The California Report",
"officialWebsiteLink": "/californiareport",
"meta": {
"site": "news",
"source": "kqed",
"order": 8
},
"link": "/californiareport",
"subscribe": {
"apple": "https://podcasts.apple.com/us/podcast/kqeds-the-california-report/id79681292",
"amazon": "https://music.amazon.com/podcasts/26099305-72af-4542-9dde-ac1807fe36d5/kqed-s-the-california-report",
"npr": "https://www.npr.org/podcasts/432285393/the-california-report",
"stitcher": "https://www.stitcher.com/podcast/kqedfm-kqeds-the-california-report-podcast-8838",
"rss": "https://ww2.kqed.org/news/tag/tcram/feed/podcast"
}
},
"californiareportmagazine": {
"id": "californiareportmagazine",
"title": "The California Report Magazine",
"tagline": "Your state, your stories",
"info": "Every week, The California Report Magazine takes you on a road trip for the ears: to visit the places and meet the people who make California unique. The in-depth storytelling podcast from the California Report.",
"airtime": "FRI 4:30pm-5pm, 6:30pm-7pm, 11pm-11:30pm",
"imageSrc": "https://cdn.kqed.org/wp-content/uploads/2024/04/The-California-Report-Magazine-Podcast-Tile-703x703-1.jpg",
"imageAlt": "KQED The California Report Magazine",
"officialWebsiteLink": "/californiareportmagazine",
"meta": {
"site": "news",
"source": "kqed",
"order": 10
},
"link": "/californiareportmagazine",
"subscribe": {
"apple": "https://podcasts.apple.com/us/podcast/the-california-report-magazine/id1314750545",
"google": "https://podcasts.google.com/feed/aHR0cHM6Ly9mZWVkcy5tZWdhcGhvbmUuZm0vS1FJTkM3NjkwNjk1OTAz",
"npr": "https://www.npr.org/podcasts/564733126/the-california-report-magazine",
"stitcher": "https://www.stitcher.com/podcast/kqed/the-california-report-magazine",
"rss": "https://ww2.kqed.org/news/tag/tcrmag/feed/podcast"
}
},
"city-arts": {
"id": "city-arts",
"title": "City Arts & Lectures",
"info": "A one-hour radio program to hear celebrated writers, artists and thinkers address contemporary ideas and values, often discussing the creative process. Please note: tapes or transcripts are not available",
"imageSrc": "https://ww2.kqed.org/radio/wp-content/uploads/sites/50/2018/05/cityartsandlecture-300x300.jpg",
"officialWebsiteLink": "https://www.cityarts.net/",
"airtime": "SUN 1pm-2pm, TUE 10pm, WED 1am",
"meta": {
"site": "news",
"source": "City Arts & Lectures"
},
"link": "https://www.cityarts.net",
"subscribe": {
"tuneIn": "https://tunein.com/radio/City-Arts-and-Lectures-p692/",
"rss": "https://www.cityarts.net/feed/"
}
},
"closealltabs": {
"id": "closealltabs",
"title": "Close All Tabs",
"tagline": "Your irreverent guide to the trends redefining our world",
"info": "Close All Tabs breaks down how digital culture shapes our world through thoughtful insights and irreverent humor.",
"imageSrc": "https://cdn.kqed.org/wp-content/uploads/2025/02/CAT_2_Tile-scaled.jpg",
"imageAlt": "KQED Close All Tabs",
"officialWebsiteLink": "/podcasts/closealltabs",
"meta": {
"site": "news",
"source": "kqed",
"order": 1
},
"link": "/podcasts/closealltabs",
"subscribe": {
"apple": "https://podcasts.apple.com/us/podcast/close-all-tabs/id214663465",
"rss": "https://feeds.megaphone.fm/KQINC6993880386",
"amazon": "https://music.amazon.com/podcasts/92d9d4ac-67a3-4eed-b10a-fb45d45b1ef2/close-all-tabs",
"spotify": "https://open.spotify.com/show/6LAJFHnGK1pYXYzv6SIol6?si=deb0cae19813417c"
}
},
"code-switch-life-kit": {
"id": "code-switch-life-kit",
"title": "Code Switch / Life Kit",
"info": "\u003cem>Code Switch\u003c/em>, which listeners will hear in the first part of the hour, has fearless and much-needed conversations about race. Hosted by journalists of color, the show tackles the subject of race head-on, exploring how it impacts every part of society — from politics and pop culture to history, sports and more.\u003cbr />\u003cbr />\u003cem>Life Kit\u003c/em>, which will be in the second part of the hour, guides you through spaces and feelings no one prepares you for — from finances to mental health, from workplace microaggressions to imposter syndrome, from relationships to parenting. The show features experts with real world experience and shares their knowledge. Because everyone needs a little help being human.\u003cbr />\u003cbr />\u003ca href=\"https://www.npr.org/podcasts/510312/codeswitch\">\u003cem>Code Switch\u003c/em> offical site and podcast\u003c/a>\u003cbr />\u003ca href=\"https://www.npr.org/lifekit\">\u003cem>Life Kit\u003c/em> offical site and podcast\u003c/a>\u003cbr />",
"airtime": "SUN 9pm-10pm",
"imageSrc": "https://cdn.kqed.org/wp-content/uploads/2024/04/Code-Switch-Life-Kit-Podcast-Tile-360x360-1.jpg",
"meta": {
"site": "radio",
"source": "npr"
},
"link": "/radio/program/code-switch-life-kit",
"subscribe": {
"apple": "https://podcasts.apple.com/podcast/1112190608?mt=2&at=11l79Y&ct=nprdirectory",
"google": "https://podcasts.google.com/feed/aHR0cHM6Ly93d3cubnByLm9yZy9yc3MvcG9kY2FzdC5waHA_aWQ9NTEwMzEy",
"spotify": "https://open.spotify.com/show/3bExJ9JQpkwNhoHvaIIuyV",
"rss": "https://feeds.npr.org/510312/podcast.xml"
}
},
"commonwealth-club": {
"id": "commonwealth-club",
"title": "Commonwealth Club of California Podcast",
"info": "The Commonwealth Club of California is the nation's oldest and largest public affairs forum. As a non-partisan forum, The Club brings to the public airwaves diverse viewpoints on important topics. The Club's weekly radio broadcast - the oldest in the U.S., dating back to 1924 - is carried across the nation on public radio stations and is now podcasting. Our website archive features audio of our recent programs, as well as selected speeches from our long and distinguished history. This podcast feed is usually updated twice a week and is always un-edited.",
"airtime": "THU 10pm, FRI 1am",
"imageSrc": "https://cdn.kqed.org/wp-content/uploads/2024/04/Commonwealth-Club-Podcast-Tile-360x360-1.jpg",
"officialWebsiteLink": "https://www.commonwealthclub.org/podcasts",
"meta": {
"site": "news",
"source": "Commonwealth Club of California"
},
"link": "/radio/program/commonwealth-club",
"subscribe": {
"apple": "https://itunes.apple.com/us/podcast/commonwealth-club-of-california-podcast/id976334034?mt=2",
"google": "https://podcasts.google.com/feed/aHR0cDovL3d3dy5jb21tb253ZWFsdGhjbHViLm9yZy9hdWRpby9wb2RjYXN0L3dlZWtseS54bWw",
"tuneIn": "https://tunein.com/radio/Commonwealth-Club-of-California-p1060/"
}
},
"forum": {
"id": "forum",
"title": "Forum",
"tagline": "The conversation starts here",
"info": "KQED’s live call-in program discussing local, state, national and international issues, as well as in-depth interviews.",
"airtime": "MON-FRI 9am-11am, 10pm-11pm",
"imageSrc": "https://cdn.kqed.org/wp-content/uploads/2024/04/Forum-Podcast-Tile-703x703-1.jpg",
"imageAlt": "KQED Forum with Mina Kim and Alexis Madrigal",
"officialWebsiteLink": "/forum",
"meta": {
"site": "news",
"source": "kqed",
"order": 9
},
"link": "/forum",
"subscribe": {
"apple": "https://podcasts.apple.com/us/podcast/kqeds-forum/id73329719",
"google": "https://podcasts.google.com/feed/aHR0cHM6Ly9mZWVkcy5tZWdhcGhvbmUuZm0vS1FJTkM5NTU3MzgxNjMz",
"npr": "https://www.npr.org/podcasts/432307980/forum",
"stitcher": "https://www.stitcher.com/podcast/kqedfm-kqeds-forum-podcast",
"rss": "https://feeds.megaphone.fm/KQINC9557381633"
}
},
"freakonomics-radio": {
"id": "freakonomics-radio",
"title": "Freakonomics Radio",
"info": "Freakonomics Radio is a one-hour award-winning podcast and public-radio project hosted by Stephen Dubner, with co-author Steve Levitt as a regular guest. It is produced in partnership with WNYC.",
"imageSrc": "https://ww2.kqed.org/news/wp-content/uploads/sites/10/2018/05/freakonomicsRadio.png",
"officialWebsiteLink": "http://freakonomics.com/",
"airtime": "SUN 1am-2am, SAT 3pm-4pm",
"meta": {
"site": "radio",
"source": "WNYC"
},
"link": "/radio/program/freakonomics-radio",
"subscribe": {
"npr": "https://rpb3r.app.goo.gl/4s8b",
"apple": "https://itunes.apple.com/us/podcast/freakonomics-radio/id354668519",
"tuneIn": "https://tunein.com/podcasts/WNYC-Podcasts/Freakonomics-Radio-p272293/",
"rss": "https://feeds.feedburner.com/freakonomicsradio"
}
},
"fresh-air": {
"id": "fresh-air",
"title": "Fresh Air",
"info": "Hosted by Terry Gross, \u003cem>Fresh Air from WHYY\u003c/em> is the Peabody Award-winning weekday magazine of contemporary arts and issues. One of public radio's most popular programs, Fresh Air features intimate conversations with today's biggest luminaries.",
"airtime": "MON-FRI 7pm-8pm",
"imageSrc": "https://cdn.kqed.org/wp-content/uploads/2024/04/Fresh-Air-Podcast-Tile-360x360-1.jpg",
"officialWebsiteLink": "https://www.npr.org/programs/fresh-air/",
"meta": {
"site": "radio",
"source": "npr"
},
"link": "/radio/program/fresh-air",
"subscribe": {
"npr": "https://rpb3r.app.goo.gl/4s8b",
"apple": "https://itunes.apple.com/WebObjects/MZStore.woa/wa/viewPodcast?s=143441&mt=2&id=214089682&at=11l79Y&ct=nprdirectory",
"tuneIn": "https://tunein.com/radio/Fresh-Air-p17/",
"rss": "https://feeds.npr.org/381444908/podcast.xml"
}
},
"here-and-now": {
"id": "here-and-now",
"title": "Here & Now",
"info": "A live production of NPR and WBUR Boston, in collaboration with stations across the country, Here & Now reflects the fluid world of news as it's happening in the middle of the day, with timely, in-depth news, interviews and conversation. Hosted by Robin Young, Jeremy Hobson and Tonya Mosley.",
"airtime": "MON-THU 11am-12pm",
"imageSrc": "https://cdn.kqed.org/wp-content/uploads/2024/04/Here-And-Now-Podcast-Tile-360x360-1.jpg",
"officialWebsiteLink": "http://www.wbur.org/hereandnow",
"meta": {
"site": "news",
"source": "npr"
},
"link": "/radio/program/here-and-now",
"subsdcribe": {
"apple": "https://itunes.apple.com/WebObjects/MZStore.woa/wa/viewPodcast?mt=2&id=426698661",
"tuneIn": "https://tunein.com/radio/Here--Now-p211/",
"rss": "https://feeds.npr.org/510051/podcast.xml"
}
},
"hidden-brain": {
"id": "hidden-brain",
"title": "Hidden Brain",
"info": "Shankar Vedantam uses science and storytelling to reveal the unconscious patterns that drive human behavior, shape our choices and direct our relationships.",
"imageSrc": "https://ww2.kqed.org/radio/wp-content/uploads/sites/50/2018/05/hiddenbrain.jpg",
"officialWebsiteLink": "https://www.npr.org/series/423302056/hidden-brain",
"airtime": "SUN 7pm-8pm",
"meta": {
"site": "news",
"source": "NPR"
},
"link": "/radio/program/hidden-brain",
"subscribe": {
"apple": "https://itunes.apple.com/us/podcast/hidden-brain/id1028908750?mt=2",
"tuneIn": "https://tunein.com/podcasts/Science-Podcasts/Hidden-Brain-p787503/",
"rss": "https://feeds.npr.org/510308/podcast.xml"
}
},
"how-i-built-this": {
"id": "how-i-built-this",
"title": "How I Built This with Guy Raz",
"info": "Guy Raz dives into the stories behind some of the world's best known companies. How I Built This weaves a narrative journey about innovators, entrepreneurs and idealists—and the movements they built.",
"imageSrc": "https://ww2.kqed.org/news/wp-content/uploads/sites/10/2018/05/howIBuiltThis.png",
"officialWebsiteLink": "https://www.npr.org/podcasts/510313/how-i-built-this",
"airtime": "SUN 7:30pm-8pm",
"meta": {
"site": "news",
"source": "npr"
},
"link": "/radio/program/how-i-built-this",
"subscribe": {
"npr": "https://rpb3r.app.goo.gl/3zxy",
"apple": "https://itunes.apple.com/us/podcast/how-i-built-this-with-guy-raz/id1150510297?mt=2",
"tuneIn": "https://tunein.com/podcasts/Arts--Culture-Podcasts/How-I-Built-This-p910896/",
"rss": "https://feeds.npr.org/510313/podcast.xml"
}
},
"hyphenacion": {
"id": "hyphenacion",
"title": "Hyphenación",
"tagline": "Where conversation and cultura meet",
"info": "What kind of no sabo word is Hyphenación? For us, it’s about living within a hyphenation. Like being a third-gen Mexican-American from the Texas border now living that Bay Area Chicano life. Like Xorje! Each week we bring together a couple of hyphenated Latinos to talk all about personal life choices: family, careers, relationships, belonging … everything is on the table. ",
"imageSrc": "https://cdn.kqed.org/wp-content/uploads/2025/03/Hyphenacion_FinalAssets_PodcastTile.png",
"imageAlt": "KQED Hyphenación",
"officialWebsiteLink": "/podcasts/hyphenacion",
"meta": {
"site": "news",
"source": "kqed",
"order": 15
},
"link": "/podcasts/hyphenacion",
"subscribe": {
"apple": "https://podcasts.apple.com/us/podcast/hyphenaci%C3%B3n/id1191591838",
"spotify": "https://open.spotify.com/show/2p3Fifq96nw9BPcmFdIq0o?si=39209f7b25774f38",
"youtube": "https://www.youtube.com/c/kqedarts",
"amazon": "https://music.amazon.com/podcasts/6c3dd23c-93fb-4aab-97ba-1725fa6315f1/hyphenaci%C3%B3n",
"rss": "https://feeds.megaphone.fm/KQINC2275451163"
}
},
"jerrybrown": {
"id": "jerrybrown",
"title": "The Political Mind of Jerry Brown",
"tagline": "Lessons from a lifetime in politics",
"info": "The Political Mind of Jerry Brown brings listeners the wisdom of the former Governor, Mayor, and presidential candidate. Scott Shafer interviewed Brown for more than 40 hours, covering the former governor's life and half-century in the political game – and Brown has some lessons he'd like to share. ",
"imageSrc": "https://cdn.kqed.org/wp-content/uploads/2024/04/The-Political-Mind-of-Jerry-Brown-Podcast-Tile-703x703-1.jpg",
"imageAlt": "KQED The Political Mind of Jerry Brown",
"officialWebsiteLink": "/podcasts/jerrybrown",
"meta": {
"site": "news",
"source": "kqed",
"order": 18
},
"link": "/podcasts/jerrybrown",
"subscribe": {
"npr": "https://www.npr.org/podcasts/790253322/the-political-mind-of-jerry-brown",
"apple": "https://itunes.apple.com/us/podcast/id1492194549",
"rss": "https://ww2.kqed.org/news/series/jerrybrown/feed/podcast/",
"tuneIn": "http://tun.in/pjGcK",
"stitcher": "https://www.stitcher.com/podcast/kqed/the-political-mind-of-jerry-brown",
"spotify": "https://open.spotify.com/show/54C1dmuyFyKMFttY6X2j6r?si=K8SgRCoISNK6ZbjpXrX5-w",
"amazon": "https://music.amazon.com/podcasts/44420f75-3b0e-4301-ab3b-16da6b09e543/the-political-mind-of-jerry-brown"
}
},
"latino-usa": {
"id": "latino-usa",
"title": "Latino USA",
"airtime": "MON 1am-2am, SUN 6pm-7pm",
"info": "Latino USA, the radio journal of news and culture, is the only national, English-language radio program produced from a Latino perspective.",
"imageSrc": "https://ww2.kqed.org/radio/wp-content/uploads/sites/50/2018/04/latinoUsa.jpg",
"officialWebsiteLink": "http://latinousa.org/",
"meta": {
"site": "news",
"source": "npr"
},
"link": "/radio/program/latino-usa",
"subscribe": {
"npr": "https://rpb3r.app.goo.gl/xtTd",
"apple": "https://itunes.apple.com/WebObjects/MZStore.woa/wa/viewPodcast?s=143441&mt=2&id=79681317&at=11l79Y&ct=nprdirectory",
"tuneIn": "https://tunein.com/radio/Latino-USA-p621/",
"rss": "https://feeds.npr.org/510016/podcast.xml"
}
},
"marketplace": {
"id": "marketplace",
"title": "Marketplace",
"info": "Our flagship program, helmed by Kai Ryssdal, examines what the day in money delivered, through stories, conversations, newsworthy numbers and more. Updated Monday through Friday at about 3:30 p.m. PT.",
"airtime": "MON-FRI 4pm-4:30pm, MON-WED 6:30pm-7pm",
"imageSrc": "https://cdn.kqed.org/wp-content/uploads/2024/04/Marketplace-Podcast-Tile-360x360-1.jpg",
"officialWebsiteLink": "https://www.marketplace.org/",
"meta": {
"site": "news",
"source": "American Public Media"
},
"link": "/radio/program/marketplace",
"subscribe": {
"apple": "https://itunes.apple.com/WebObjects/MZStore.woa/wa/viewPodcast?s=143441&mt=2&id=201853034&at=11l79Y&ct=nprdirectory",
"tuneIn": "https://tunein.com/radio/APM-Marketplace-p88/",
"rss": "https://feeds.publicradio.org/public_feeds/marketplace-pm/rss/rss"
}
},
"masters-of-scale": {
"id": "masters-of-scale",
"title": "Masters of Scale",
"info": "Masters of Scale is an original podcast in which LinkedIn co-founder and Greylock Partner Reid Hoffman sets out to describe and prove theories that explain how great entrepreneurs take their companies from zero to a gazillion in ingenious fashion.",
"airtime": "Every other Wednesday June 12 through October 16 at 8pm (repeats Thursdays at 2am)",
"imageSrc": "https://cdn.kqed.org/wp-content/uploads/2024/04/Masters-of-Scale-Podcast-Tile-360x360-1.jpg",
"officialWebsiteLink": "https://mastersofscale.com/",
"meta": {
"site": "radio",
"source": "WaitWhat"
},
"link": "/radio/program/masters-of-scale",
"subscribe": {
"apple": "http://mastersofscale.app.link/",
"rss": "https://rss.art19.com/masters-of-scale"
}
},
"mindshift": {
"id": "mindshift",
"title": "MindShift",
"tagline": "A podcast about the future of learning and how we raise our kids",
"info": "The MindShift podcast explores the innovations in education that are shaping how kids learn. Hosts Ki Sung and Katrina Schwartz introduce listeners to educators, researchers, parents and students who are developing effective ways to improve how kids learn. We cover topics like how fed-up administrators are developing surprising tactics to deal with classroom disruptions; how listening to podcasts are helping kids develop reading skills; the consequences of overparenting; and why interdisciplinary learning can engage students on all ends of the traditional achievement spectrum. This podcast is part of the MindShift education site, a division of KQED News. KQED is an NPR/PBS member station based in San Francisco. You can also visit the MindShift website for episodes and supplemental blog posts or tweet us \u003ca href=\"https://twitter.com/MindShiftKQED\">@MindShiftKQED\u003c/a> or visit us at \u003ca href=\"/mindshift\">MindShift.KQED.org\u003c/a>",
"imageSrc": "https://cdn.kqed.org/wp-content/uploads/2024/04/Mindshift-Podcast-Tile-703x703-1.jpg",
"imageAlt": "KQED MindShift: How We Will Learn",
"officialWebsiteLink": "/mindshift/",
"meta": {
"site": "news",
"source": "kqed",
"order": 12
},
"link": "/podcasts/mindshift",
"subscribe": {
"apple": "https://podcasts.apple.com/us/podcast/mindshift-podcast/id1078765985",
"google": "https://podcasts.google.com/feed/aHR0cHM6Ly9mZWVkcy5tZWdhcGhvbmUuZm0vS1FJTkM1NzY0NjAwNDI5",
"npr": "https://www.npr.org/podcasts/464615685/mind-shift-podcast",
"stitcher": "https://www.stitcher.com/podcast/kqed/stories-teachers-share",
"spotify": "https://open.spotify.com/show/0MxSpNYZKNprFLCl7eEtyx"
}
},
"morning-edition": {
"id": "morning-edition",
"title": "Morning Edition",
"info": "\u003cem>Morning Edition\u003c/em> takes listeners around the country and the world with multi-faceted stories and commentaries every weekday. Hosts Steve Inskeep, David Greene and Rachel Martin bring you the latest breaking news and features to prepare you for the day.",
"airtime": "MON-FRI 3am-9am",
"imageSrc": "https://cdn.kqed.org/wp-content/uploads/2024/04/Morning-Edition-Podcast-Tile-360x360-1.jpg",
"officialWebsiteLink": "https://www.npr.org/programs/morning-edition/",
"meta": {
"site": "news",
"source": "npr"
},
"link": "/radio/program/morning-edition"
},
"onourwatch": {
"id": "onourwatch",
"title": "On Our Watch",
"tagline": "Deeply-reported investigative journalism",
"info": "For decades, the process for how police police themselves has been inconsistent – if not opaque. In some states, like California, these proceedings were completely hidden. After a new police transparency law unsealed scores of internal affairs files, our reporters set out to examine these cases and the shadow world of police discipline. On Our Watch brings listeners into the rooms where officers are questioned and witnesses are interrogated to find out who this system is really protecting. Is it the officers, or the public they've sworn to serve?",
"imageSrc": "https://cdn.kqed.org/wp-content/uploads/2024/04/On-Our-Watch-Podcast-Tile-703x703-1.jpg",
"imageAlt": "On Our Watch from NPR and KQED",
"officialWebsiteLink": "/podcasts/onourwatch",
"meta": {
"site": "news",
"source": "kqed",
"order": 11
},
"link": "/podcasts/onourwatch",
"subscribe": {
"apple": "https://podcasts.apple.com/podcast/id1567098962",
"google": "https://podcasts.google.com/feed/aHR0cHM6Ly9mZWVkcy5ucHIub3JnLzUxMDM2MC9wb2RjYXN0LnhtbD9zYz1nb29nbGVwb2RjYXN0cw",
"npr": "https://rpb3r.app.goo.gl/onourwatch",
"spotify": "https://open.spotify.com/show/0OLWoyizopu6tY1XiuX70x",
"tuneIn": "https://tunein.com/radio/On-Our-Watch-p1436229/",
"stitcher": "https://www.stitcher.com/show/on-our-watch",
"rss": "https://feeds.npr.org/510360/podcast.xml"
}
},
"on-the-media": {
"id": "on-the-media",
"title": "On The Media",
"info": "Our weekly podcast explores how the media 'sausage' is made, casts an incisive eye on fluctuations in the marketplace of ideas, and examines threats to the freedom of information and expression in America and abroad. For one hour a week, the show tries to lift the veil from the process of \"making media,\" especially news media, because it's through that lens that we see the world and the world sees us",
"airtime": "SUN 2pm-3pm, MON 12am-1am",
"imageSrc": "https://ww2.kqed.org/radio/wp-content/uploads/sites/50/2018/04/onTheMedia.png",
"officialWebsiteLink": "https://www.wnycstudios.org/shows/otm",
"meta": {
"site": "news",
"source": "wnyc"
},
"link": "/radio/program/on-the-media",
"subscribe": {
"apple": "https://itunes.apple.com/us/podcast/on-the-media/id73330715?mt=2",
"tuneIn": "https://tunein.com/radio/On-the-Media-p69/",
"rss": "http://feeds.wnyc.org/onthemedia"
}
},
"pbs-newshour": {
"id": "pbs-newshour",
"title": "PBS NewsHour",
"info": "Analysis, background reports and updates from the PBS NewsHour putting today's news in context.",
"airtime": "MON-FRI 3pm-4pm",
"imageSrc": "https://cdn.kqed.org/wp-content/uploads/2024/04/PBS-News-Hour-Podcast-Tile-360x360-1.jpg",
"officialWebsiteLink": "https://www.pbs.org/newshour/",
"meta": {
"site": "news",
"source": "pbs"
},
"link": "/radio/program/pbs-newshour",
"subscribe": {
"apple": "https://itunes.apple.com/us/podcast/pbs-newshour-full-show/id394432287?mt=2",
"tuneIn": "https://tunein.com/radio/PBS-NewsHour---Full-Show-p425698/",
"rss": "https://www.pbs.org/newshour/feeds/rss/podcasts/show"
}
},
"perspectives": {
"id": "perspectives",
"title": "Perspectives",
"tagline": "KQED's series of daily listener commentaries since 1991",
"info": "KQED's series of daily listener commentaries since 1991.",
"imageSrc": "https://cdn.kqed.org/wp-content/uploads/2025/01/Perspectives_Tile_Final.jpg",
"imageAlt": "KQED Perspectives",
"officialWebsiteLink": "/perspectives/",
"meta": {
"site": "radio",
"source": "kqed",
"order": 14
},
"link": "/perspectives",
"subscribe": {
"apple": "https://podcasts.apple.com/us/podcast/id73801135",
"npr": "https://www.npr.org/podcasts/432309616/perspectives",
"rss": "https://ww2.kqed.org/perspectives/category/perspectives/feed/",
"google": "https://podcasts.google.com/feed/aHR0cHM6Ly93dzIua3FlZC5vcmcvcGVyc3BlY3RpdmVzL2NhdGVnb3J5L3BlcnNwZWN0aXZlcy9mZWVkLw"
}
},
"planet-money": {
"id": "planet-money",
"title": "Planet Money",
"info": "The economy explained. Imagine you could call up a friend and say, Meet me at the bar and tell me what's going on with the economy. Now imagine that's actually a fun evening.",
"airtime": "SUN 3pm-4pm",
"imageSrc": "https://ww2.kqed.org/radio/wp-content/uploads/sites/50/2018/04/planetmoney.jpg",
"officialWebsiteLink": "https://www.npr.org/sections/money/",
"meta": {
"site": "news",
"source": "npr"
},
"link": "/radio/program/planet-money",
"subscribe": {
"npr": "https://rpb3r.app.goo.gl/M4f5",
"apple": "https://itunes.apple.com/us/podcast/planet-money/id290783428?mt=2",
"tuneIn": "https://tunein.com/podcasts/Business--Economics-Podcasts/Planet-Money-p164680/",
"rss": "https://feeds.npr.org/510289/podcast.xml"
}
},
"politicalbreakdown": {
"id": "politicalbreakdown",
"title": "Political Breakdown",
"tagline": "Politics from a personal perspective",
"info": "Political Breakdown is a new series that explores the political intersection of California and the nation. Each week hosts Scott Shafer and Marisa Lagos are joined with a new special guest to unpack politics -- with personality — and offer an insider’s glimpse at how politics happens.",
"airtime": "THU 6:30pm-7pm",
"imageSrc": "https://cdn.kqed.org/wp-content/uploads/2024/04/Political-Breakdown-2024-Podcast-Tile-703x703-1.jpg",
"imageAlt": "KQED Political Breakdown",
"officialWebsiteLink": "/podcasts/politicalbreakdown",
"meta": {
"site": "radio",
"source": "kqed",
"order": 5
},
"link": "/podcasts/politicalbreakdown",
"subscribe": {
"apple": "https://podcasts.apple.com/us/podcast/political-breakdown/id1327641087",
"amazon": "https://music.amazon.com/podcasts/e0c2d153-ad36-4c8d-901d-f1da6a724824/political-breakdown",
"npr": "https://www.npr.org/podcasts/572155894/political-breakdown",
"stitcher": "https://www.stitcher.com/podcast/kqed/political-breakdown",
"spotify": "https://open.spotify.com/show/07RVyIjIdk2WDuVehvBMoN",
"rss": "https://ww2.kqed.org/news/tag/political-breakdown/feed/podcast"
}
},
"possible": {
"id": "possible",
"title": "Possible",
"info": "Possible is hosted by entrepreneur Reid Hoffman and writer Aria Finger. Together in Possible, Hoffman and Finger lead enlightening discussions about building a brighter collective future. The show features interviews with visionary guests like Trevor Noah, Sam Altman and Janette Sadik-Khan. Possible paints an optimistic portrait of the world we can create through science, policy, business, art and our shared humanity. It asks: What if everything goes right for once? How can we get there? Each episode also includes a short fiction story generated by advanced AI GPT-4, serving as a thought-provoking springboard to speculate how humanity could leverage technology for good.",
"airtime": "SUN 2pm",
"imageSrc": "https://cdn.kqed.org/wp-content/uploads/2024/04/Possible-Podcast-Tile-360x360-1.jpg",
"officialWebsiteLink": "https://www.possible.fm/",
"meta": {
"site": "news",
"source": "Possible"
},
"link": "/radio/program/possible",
"subscribe": {
"apple": "https://podcasts.apple.com/us/podcast/possible/id1677184070",
"spotify": "https://open.spotify.com/show/730YpdUSNlMyPQwNnyjp4k"
}
},
"pri-the-world": {
"id": "pri-the-world",
"title": "PRI's The World: Latest Edition",
"info": "Each weekday, host Marco Werman and his team of producers bring you the world's most interesting stories in an hour of radio that reminds us just how small our planet really is.",
"airtime": "MON-FRI 2pm-3pm",
"imageSrc": "https://cdn.kqed.org/wp-content/uploads/2024/04/The-World-Podcast-Tile-360x360-1.jpg",
"officialWebsiteLink": "https://www.pri.org/programs/the-world",
"meta": {
"site": "news",
"source": "PRI"
},
"link": "/radio/program/pri-the-world",
"subscribe": {
"apple": "https://itunes.apple.com/us/podcast/pris-the-world-latest-edition/id278196007?mt=2",
"tuneIn": "https://tunein.com/podcasts/News--Politics-Podcasts/PRIs-The-World-p24/",
"rss": "http://feeds.feedburner.com/pri/theworld"
}
},
"radiolab": {
"id": "radiolab",
"title": "Radiolab",
"info": "A two-time Peabody Award-winner, Radiolab is an investigation told through sounds and stories, and centered around one big idea. In the Radiolab world, information sounds like music and science and culture collide. Hosted by Jad Abumrad and Robert Krulwich, the show is designed for listeners who demand skepticism, but appreciate wonder. WNYC Studios is the producer of other leading podcasts including Freakonomics Radio, Death, Sex & Money, On the Media and many more.",
"airtime": "SUN 12am-1am, SAT 2pm-3pm",
"imageSrc": "https://ww2.kqed.org/radio/wp-content/uploads/sites/50/2018/04/radiolab1400.png",
"officialWebsiteLink": "https://www.wnycstudios.org/shows/radiolab/",
"meta": {
"site": "science",
"source": "WNYC"
},
"link": "/radio/program/radiolab",
"subscribe": {
"apple": "https://itunes.apple.com/us/podcast/radiolab/id152249110?mt=2",
"tuneIn": "https://tunein.com/radio/RadioLab-p68032/",
"rss": "https://feeds.wnyc.org/radiolab"
}
},
"reveal": {
"id": "reveal",
"title": "Reveal",
"info": "Created by The Center for Investigative Reporting and PRX, Reveal is public radios first one-hour weekly radio show and podcast dedicated to investigative reporting. Credible, fact based and without a partisan agenda, Reveal combines the power and artistry of driveway moment storytelling with data-rich reporting on critically important issues. The result is stories that inform and inspire, arming our listeners with information to right injustices, hold the powerful accountable and improve lives.Reveal is hosted by Al Letson and showcases the award-winning work of CIR and newsrooms large and small across the nation. In a radio and podcast market crowded with choices, Reveal focuses on important and often surprising stories that illuminate the world for our listeners.",
"airtime": "SAT 4pm-5pm",
"imageSrc": "https://ww2.kqed.org/radio/wp-content/uploads/sites/50/2018/04/reveal300px.png",
"officialWebsiteLink": "https://www.revealnews.org/episodes/",
"meta": {
"site": "news",
"source": "npr"
},
"link": "/radio/program/reveal",
"subscribe": {
"apple": "https://itunes.apple.com/us/podcast/reveal/id886009669",
"tuneIn": "https://tunein.com/radio/Reveal-p679597/",
"rss": "http://feeds.revealradio.org/revealpodcast"
}
},
"rightnowish": {
"id": "rightnowish",
"title": "Rightnowish",
"tagline": "Art is where you find it",
"info": "Rightnowish digs into life in the Bay Area right now… ish. Journalist Pendarvis Harshaw takes us to galleries painted on the sides of liquor stores in West Oakland. We'll dance in warehouses in the Bayview, make smoothies with kids in South Berkeley, and listen to classical music in a 1984 Cutlass Supreme in Richmond. Every week, Pen talks to movers and shakers about how the Bay Area shapes what they create, and how they shape the place we call home.",
"imageSrc": "https://cdn.kqed.org/wp-content/uploads/2024/04/Rightnowish-Podcast-Tile-500x500-1.jpg",
"imageAlt": "KQED Rightnowish with Pendarvis Harshaw",
"officialWebsiteLink": "/podcasts/rightnowish",
"meta": {
"site": "arts",
"source": "kqed",
"order": 16
},
"link": "/podcasts/rightnowish",
"subscribe": {
"npr": "https://www.npr.org/podcasts/721590300/rightnowish",
"rss": "https://ww2.kqed.org/arts/programs/rightnowish/feed/podcast",
"apple": "https://podcasts.apple.com/us/podcast/rightnowish/id1482187648",
"stitcher": "https://www.stitcher.com/podcast/kqed/rightnowish",
"google": "https://podcasts.google.com/feed/aHR0cHM6Ly9mZWVkcy5tZWdhcGhvbmUuZm0vS1FJTkMxMjU5MTY3NDc4",
"spotify": "https://open.spotify.com/show/7kEJuafTzTVan7B78ttz1I"
}
},
"science-friday": {
"id": "science-friday",
"title": "Science Friday",
"info": "Science Friday is a weekly science talk show, broadcast live over public radio stations nationwide. Each week, the show focuses on science topics that are in the news and tries to bring an educated, balanced discussion to bear on the scientific issues at hand. Panels of expert guests join host Ira Flatow, a veteran science journalist, to discuss science and to take questions from listeners during the call-in portion of the program.",
"airtime": "FRI 11am-1pm",
"imageSrc": "https://cdn.kqed.org/wp-content/uploads/2024/04/Science-Friday-Podcast-Tile-360x360-1.jpg",
"officialWebsiteLink": "https://www.wnycstudios.org/shows/science-friday",
"meta": {
"site": "news",
"source": "npr"
},
"link": "/radio/program/science-friday",
"subscribe": {
"apple": "https://itunes.apple.com/WebObjects/MZStore.woa/wa/viewPodcast?s=143441&mt=2&id=73329284&at=11l79Y&ct=nprdirectory",
"tuneIn": "https://tunein.com/radio/Science-Friday-p394/",
"rss": "http://feeds.wnyc.org/science-friday"
}
},
"snap-judgment": {
"id": "snap-judgment",
"title": "Snap Judgment",
"tagline": "Real stories with killer beats",
"info": "The Snap Judgment radio show and podcast mixes real stories with killer beats to produce cinematic, dramatic radio. Snap's musical brand of storytelling dares listeners to see the world through the eyes of another. This is storytelling... with a BEAT!! Snap first aired on public radio stations nationwide in July 2010. Today, Snap Judgment airs on over 450 public radio stations and is brought to the airwaves by KQED & PRX.",
"airtime": "SAT 1pm-2pm, 9pm-10pm",
"imageSrc": "https://cdn.kqed.org/wp-content/uploads/2024/05/Snap-Judgment-Podcast-Tile-703x703-1.jpg",
"imageAlt": "KQED Snap Judgment",
"officialWebsiteLink": "https://snapjudgment.org",
"meta": {
"site": "arts",
"source": "kqed",
"order": 4
},
"link": "https://snapjudgment.org",
"subscribe": {
"apple": "https://podcasts.apple.com/us/podcast/snap-judgment/id283657561",
"npr": "https://www.npr.org/podcasts/449018144/snap-judgment",
"stitcher": "https://www.pandora.com/podcast/snap-judgment/PC:241?source=stitcher-sunset",
"spotify": "https://open.spotify.com/show/3Cct7ZWmxHNAtLgBTqjC5v",
"rss": "https://snap.feed.snapjudgment.org/"
}
},
"soldout": {
"id": "soldout",
"title": "SOLD OUT: Rethinking Housing in America",
"tagline": "A new future for housing",
"info": "Sold Out: Rethinking Housing in America",
"imageSrc": "https://cdn.kqed.org/wp-content/uploads/2024/04/Sold-Out-Podcast-Tile-703x703-1.jpg",
"imageAlt": "KQED Sold Out: Rethinking Housing in America",
"officialWebsiteLink": "/podcasts/soldout",
"meta": {
"site": "news",
"source": "kqed",
"order": 13
},
"link": "/podcasts/soldout",
"subscribe": {
"npr": "https://www.npr.org/podcasts/911586047/s-o-l-d-o-u-t-a-new-future-for-housing",
"apple": "https://podcasts.apple.com/us/podcast/introducing-sold-out-rethinking-housing-in-america/id1531354937",
"rss": "https://feeds.megaphone.fm/soldout",
"spotify": "https://open.spotify.com/show/38dTBSk2ISFoPiyYNoKn1X",
"stitcher": "https://www.stitcher.com/podcast/kqed/sold-out-rethinking-housing-in-america",
"tunein": "https://tunein.com/radio/SOLD-OUT-Rethinking-Housing-in-America-p1365871/"
}
},
"spooked": {
"id": "spooked",
"title": "Spooked",
"tagline": "True-life supernatural stories",
"info": "",
"imageSrc": "https://cdn.kqed.org/wp-content/uploads/2024/10/Spooked-Podcast-Tile-703x703-1.jpg",
"imageAlt": "KQED Spooked",
"officialWebsiteLink": "https://spookedpodcast.org/",
"meta": {
"site": "news",
"source": "kqed",
"order": 7
},
"link": "https://spookedpodcast.org/",
"subscribe": {
"apple": "https://podcasts.apple.com/us/podcast/spooked/id1279361017",
"npr": "https://www.npr.org/podcasts/549547848/snap-judgment-presents-spooked",
"spotify": "https://open.spotify.com/show/76571Rfl3m7PLJQZKQIGCT",
"rss": "https://feeds.simplecast.com/TBotaapn"
}
},
"tech-nation": {
"id": "tech-nation",
"title": "Tech Nation Radio Podcast",
"info": "Tech Nation is a weekly public radio program, hosted by Dr. Moira Gunn. Founded in 1993, it has grown from a simple interview show to a multi-faceted production, featuring conversations with noted technology and science leaders, and a weekly science and technology-related commentary.",
"airtime": "FRI 10pm",
"imageSrc": "https://cdn.kqed.org/wp-content/uploads/2024/04/Tech-Nation-Radio-Podcast-Tile-360x360-1.jpg",
"officialWebsiteLink": "http://technation.podomatic.com/",
"meta": {
"site": "science",
"source": "Tech Nation Media"
},
"link": "/radio/program/tech-nation",
"subscribe": {
"rss": "https://technation.podomatic.com/rss2.xml"
}
},
"ted-radio-hour": {
"id": "ted-radio-hour",
"title": "TED Radio Hour",
"info": "The TED Radio Hour is a journey through fascinating ideas, astonishing inventions, fresh approaches to old problems, and new ways to think and create.",
"airtime": "SUN 3pm-4pm, SAT 10pm-11pm",
"imageSrc": "https://ww2.kqed.org/radio/wp-content/uploads/sites/50/2018/04/tedRadioHour.jpg",
"officialWebsiteLink": "https://www.npr.org/programs/ted-radio-hour/?showDate=2018-06-22",
"meta": {
"site": "news",
"source": "npr"
},
"link": "/radio/program/ted-radio-hour",
"subscribe": {
"npr": "https://rpb3r.app.goo.gl/8vsS",
"apple": "https://itunes.apple.com/WebObjects/MZStore.woa/wa/viewPodcast?s=143441&mt=2&id=523121474&at=11l79Y&ct=nprdirectory",
"tuneIn": "https://tunein.com/radio/TED-Radio-Hour-p418021/",
"rss": "https://feeds.npr.org/510298/podcast.xml"
}
},
"thebay": {
"id": "thebay",
"title": "The Bay",
"tagline": "Local news to keep you rooted",
"info": "Host Devin Katayama walks you through the biggest story of the day with reporters and newsmakers.",
"imageSrc": "https://cdn.kqed.org/wp-content/uploads/2024/04/The-Bay-Podcast-Tile-703x703-1.jpg",
"imageAlt": "KQED The Bay",
"officialWebsiteLink": "/podcasts/thebay",
"meta": {
"site": "radio",
"source": "kqed",
"order": 2
},
"link": "/podcasts/thebay",
"subscribe": {
"apple": "https://podcasts.apple.com/us/podcast/the-bay/id1350043452",
"amazon": "https://music.amazon.com/podcasts/d800ea4c-7a2c-42f2-b861-edaf78a5db0b/the-bay",
"npr": "https://www.npr.org/podcasts/586725995/the-bay",
"stitcher": "https://www.stitcher.com/podcast/kqed/the-bay",
"spotify": "https://open.spotify.com/show/4BIKBKIujizLHlIlBNaAqQ",
"rss": "https://feeds.megaphone.fm/KQINC8259786327"
}
},
"thelatest": {
"id": "thelatest",
"title": "The Latest",
"tagline": "Trusted local news in real time",
"info": "",
"imageSrc": "https://cdn.kqed.org/wp-content/uploads/2025/05/The-Latest-2025-Podcast-Tile-703x703-1.jpg",
"imageAlt": "KQED The Latest",
"officialWebsiteLink": "/thelatest",
"meta": {
"site": "news",
"source": "kqed",
"order": 6
},
"link": "/thelatest",
"subscribe": {
"apple": "https://podcasts.apple.com/us/podcast/the-latest-from-kqed/id1197721799",
"npr": "https://www.npr.org/podcasts/1257949365/the-latest-from-k-q-e-d",
"spotify": "https://open.spotify.com/show/5KIIXMgM9GTi5AepwOYvIZ?si=bd3053fec7244dba",
"rss": "https://feeds.megaphone.fm/KQINC9137121918"
}
},
"theleap": {
"id": "theleap",
"title": "The Leap",
"tagline": "What if you closed your eyes, and jumped?",
"info": "Stories about people making dramatic, risky changes, told by award-winning public radio reporter Judy Campbell.",
"imageSrc": "https://cdn.kqed.org/wp-content/uploads/2024/04/The-Leap-Podcast-Tile-703x703-1.jpg",
"imageAlt": "KQED The Leap",
"officialWebsiteLink": "/podcasts/theleap",
"meta": {
"site": "news",
"source": "kqed",
"order": 17
},
"link": "/podcasts/theleap",
"subscribe": {
"apple": "https://podcasts.apple.com/us/podcast/the-leap/id1046668171",
"npr": "https://www.npr.org/podcasts/447248267/the-leap",
"stitcher": "https://www.stitcher.com/podcast/kqed/the-leap",
"spotify": "https://open.spotify.com/show/3sSlVHHzU0ytLwuGs1SD1U",
"rss": "https://ww2.kqed.org/news/programs/the-leap/feed/podcast"
}
},
"the-moth-radio-hour": {
"id": "the-moth-radio-hour",
"title": "The Moth Radio Hour",
"info": "Since its launch in 1997, The Moth has presented thousands of true stories, told live and without notes, to standing-room-only crowds worldwide. Moth storytellers stand alone, under a spotlight, with only a microphone and a roomful of strangers. The storyteller and the audience embark on a high-wire act of shared experience which is both terrifying and exhilarating. Since 2008, The Moth podcast has featured many of our favorite stories told live on Moth stages around the country. For information on all of our programs and live events, visit themoth.org.",
"airtime": "SAT 8pm-9pm and SUN 11am-12pm",
"imageSrc": "https://ww2.kqed.org/radio/wp-content/uploads/sites/50/2018/04/theMoth.jpg",
"officialWebsiteLink": "https://themoth.org/",
"meta": {
"site": "arts",
"source": "prx"
},
"link": "/radio/program/the-moth-radio-hour",
"subscribe": {
"apple": "https://itunes.apple.com/us/podcast/the-moth-podcast/id275699983?mt=2",
"tuneIn": "https://tunein.com/radio/The-Moth-p273888/",
"rss": "http://feeds.themoth.org/themothpodcast"
}
},
"the-new-yorker-radio-hour": {
"id": "the-new-yorker-radio-hour",
"title": "The New Yorker Radio Hour",
"info": "The New Yorker Radio Hour is a weekly program presented by the magazine's editor, David Remnick, and produced by WNYC Studios and The New Yorker. Each episode features a diverse mix of interviews, profiles, storytelling, and an occasional burst of humor inspired by the magazine, and shaped by its writers, artists, and editors. This isn't a radio version of a magazine, but something all its own, reflecting the rich possibilities of audio storytelling and conversation. Theme music for the show was composed and performed by Merrill Garbus of tUnE-YArDs.",
"airtime": "SAT 10am-11am",
"imageSrc": "https://cdn.kqed.org/wp-content/uploads/2024/04/The-New-Yorker-Podcast-Tile-360x360-1.jpg",
"officialWebsiteLink": "https://www.wnycstudios.org/shows/tnyradiohour",
"meta": {
"site": "arts",
"source": "WNYC"
},
"link": "/radio/program/the-new-yorker-radio-hour",
"subscribe": {
"apple": "https://itunes.apple.com/us/podcast/id1050430296",
"tuneIn": "https://tunein.com/podcasts/WNYC-Podcasts/New-Yorker-Radio-Hour-p803804/",
"rss": "https://feeds.feedburner.com/newyorkerradiohour"
}
},
"the-sam-sanders-show": {
"id": "the-sam-sanders-show",
"title": "The Sam Sanders Show",
"info": "One of public radio's most dynamic voices, Sam Sanders helped launch The NPR Politics Podcast and hosted NPR's hit show It's Been A Minute. Now, the award-winning host returns with something brand new, The Sam Sanders Show. Every week, Sam Sanders and friends dig into the culture that shapes our lives: what's driving the biggest trends, how artists really think, and even the memes you can't stop scrolling past. Sam is beloved for his way of unpacking the world and bringing you up close to fresh currents and engaging conversations. The Sam Sanders Show is smart, funny and always a good time.",
"airtime": "FRI 12-1pm AND SAT 11am-12pm",
"imageSrc": "https://cdn.kqed.org/wp-content/uploads/2025/11/The-Sam-Sanders-Show-Podcast-Tile-400x400-1.jpg",
"officialWebsiteLink": "https://www.kcrw.com/shows/the-sam-sanders-show/latest",
"meta": {
"site": "arts",
"source": "KCRW"
},
"link": "https://www.kcrw.com/shows/the-sam-sanders-show/latest",
"subscribe": {
"rss": "https://feed.cdnstream1.com/zjb/feed/download/ac/28/59/ac28594c-e1d0-4231-8728-61865cdc80e8.xml"
}
},
"the-splendid-table": {
"id": "the-splendid-table",
"title": "The Splendid Table",
"info": "\u003cem>The Splendid Table\u003c/em> hosts our nation's conversations about cooking, sustainability and food culture.",
"imageSrc": "https://cdn.kqed.org/wp-content/uploads/2024/04/The-Splendid-Table-Podcast-Tile-360x360-1.jpg",
"officialWebsiteLink": "https://www.splendidtable.org/",
"airtime": "SUN 10-11 pm",
"meta": {
"site": "radio",
"source": "npr"
},
"link": "/radio/program/the-splendid-table"
},
"this-american-life": {
"id": "this-american-life",
"title": "This American Life",
"info": "This American Life is a weekly public radio show, heard by 2.2 million people on more than 500 stations. Another 2.5 million people download the weekly podcast. It is hosted by Ira Glass, produced in collaboration with Chicago Public Media, delivered to stations by PRX The Public Radio Exchange, and has won all of the major broadcasting awards.",
"airtime": "SAT 12pm-1pm, 7pm-8pm",
"imageSrc": "https://ww2.kqed.org/radio/wp-content/uploads/sites/50/2018/04/thisAmericanLife.png",
"officialWebsiteLink": "https://www.thisamericanlife.org/",
"meta": {
"site": "news",
"source": "wbez"
},
"link": "/radio/program/this-american-life",
"subscribe": {
"apple": "https://itunes.apple.com/WebObjects/MZStore.woa/wa/viewPodcast?s=143441&mt=2&id=201671138&at=11l79Y&ct=nprdirectory",
"rss": "https://www.thisamericanlife.org/podcast/rss.xml"
}
},
"tinydeskradio": {
"id": "tinydeskradio",
"title": "Tiny Desk Radio",
"info": "We're bringing the best of Tiny Desk to the airwaves, only on public radio.",
"airtime": "SUN 8pm and SAT 9pm",
"imageSrc": "https://cdn.kqed.org/wp-content/uploads/2025/04/300x300-For-Member-Station-Logo-Tiny-Desk-Radio-@2x.png",
"officialWebsiteLink": "https://www.npr.org/series/g-s1-52030/tiny-desk-radio",
"meta": {
"site": "news",
"source": "npr"
},
"link": "/radio/program/tinydeskradio",
"subscribe": {
"rss": "https://feeds.npr.org/g-s1-52030/rss.xml"
}
},
"wait-wait-dont-tell-me": {
"id": "wait-wait-dont-tell-me",
"title": "Wait Wait... Don't Tell Me!",
"info": "Peter Sagal and Bill Kurtis host the weekly NPR News quiz show alongside some of the best and brightest news and entertainment personalities.",
"airtime": "SUN 10am-11am, SAT 11am-12pm, SAT 6pm-7pm",
"imageSrc": "https://cdn.kqed.org/wp-content/uploads/2024/04/Wait-Wait-Podcast-Tile-300x300-1.jpg",
"officialWebsiteLink": "https://www.npr.org/programs/wait-wait-dont-tell-me/",
"meta": {
"site": "news",
"source": "npr"
},
"link": "/radio/program/wait-wait-dont-tell-me",
"subscribe": {
"npr": "https://rpb3r.app.goo.gl/Xogv",
"apple": "https://itunes.apple.com/WebObjects/MZStore.woa/wa/viewPodcast?s=143441&mt=2&id=121493804&at=11l79Y&ct=nprdirectory",
"tuneIn": "https://tunein.com/radio/Wait-Wait-Dont-Tell-Me-p46/",
"rss": "https://feeds.npr.org/344098539/podcast.xml"
}
},
"weekend-edition-saturday": {
"id": "weekend-edition-saturday",
"title": "Weekend Edition Saturday",
"info": "Weekend Edition Saturday wraps up the week's news and offers a mix of analysis and features on a wide range of topics, including arts, sports, entertainment, and human interest stories. The two-hour program is hosted by NPR's Peabody Award-winning Scott Simon.",
"airtime": "SAT 5am-10am",
"imageSrc": "https://cdn.kqed.org/wp-content/uploads/2024/04/Weekend-Edition-Podcast-Tile-360x360-1.jpg",
"officialWebsiteLink": "https://www.npr.org/programs/weekend-edition-saturday/",
"meta": {
"site": "news",
"source": "npr"
},
"link": "/radio/program/weekend-edition-saturday"
},
"weekend-edition-sunday": {
"id": "weekend-edition-sunday",
"title": "Weekend Edition Sunday",
"info": "Weekend Edition Sunday features interviews with newsmakers, artists, scientists, politicians, musicians, writers, theologians and historians. The program has covered news events from Nelson Mandela's 1990 release from a South African prison to the capture of Saddam Hussein.",
"airtime": "SUN 5am-10am",
"imageSrc": "https://cdn.kqed.org/wp-content/uploads/2024/04/Weekend-Edition-Podcast-Tile-360x360-1.jpg",
"officialWebsiteLink": "https://www.npr.org/programs/weekend-edition-sunday/",
"meta": {
"site": "news",
"source": "npr"
},
"link": "/radio/program/weekend-edition-sunday"
}
},
"racesReducer": {},
"racesGenElectionReducer": {},
"racesGenElection2026Reducer": {},
"radioSchedulesReducer": {},
"listsReducer": {
"posts/mindshift?tag=artificial-intelligence": {
"isFetching": false,
"latestQuery": {
"from": 0,
"size": 9
},
"vitalsOnly": false,
"totalRequested": 9,
"isLoading": false,
"isLoadingMore": true,
"total": {
"value": 52,
"relation": "eq"
},
"items": [
"mindshift_66459",
"mindshift_66419",
"mindshift_66379",
"mindshift_66358",
"mindshift_66311",
"mindshift_66299",
"mindshift_66289",
"mindshift_66237",
"mindshift_66217"
],
"complete": true
}
},
"recallGuideReducer": {
"intros": {},
"policy": {},
"candidates": {}
},
"savedArticleReducer": {
"articles": [],
"status": {}
},
"newslettersReducer": {
"isFetching": false,
"fetchFailed": false,
"hasFetched": false,
"newsletters": {},
"isSubscribing": false,
"isUnsubscribing": false,
"subscribedNewsletters": {}
},
"termsReducer": {
"about": {
"name": "About",
"type": "terms",
"id": "about",
"slug": "about",
"link": "/about",
"taxonomy": "site"
},
"arts": {
"name": "Arts & Culture",
"grouping": [
"arts",
"pop",
"trulyca"
],
"description": "KQED Arts provides daily in-depth coverage of the Bay Area's music, art, film, performing arts, literature and arts news, as well as cultural commentary and criticism.",
"type": "terms",
"id": "arts",
"slug": "arts",
"link": "/arts",
"taxonomy": "site"
},
"artschool": {
"name": "Art School",
"parent": "arts",
"type": "terms",
"id": "artschool",
"slug": "artschool",
"link": "/artschool",
"taxonomy": "site"
},
"bayareabites": {
"name": "KQED food",
"grouping": [
"food",
"bayareabites",
"checkplease"
],
"parent": "food",
"type": "terms",
"id": "bayareabites",
"slug": "bayareabites",
"link": "/food",
"taxonomy": "site"
},
"bayareahiphop": {
"name": "Bay Area Hiphop",
"type": "terms",
"id": "bayareahiphop",
"slug": "bayareahiphop",
"link": "/bayareahiphop",
"taxonomy": "site"
},
"campaign21": {
"name": "Campaign 21",
"type": "terms",
"id": "campaign21",
"slug": "campaign21",
"link": "/campaign21",
"taxonomy": "site"
},
"careers": {
"name": "Careers",
"type": "terms",
"id": "careers",
"slug": "careers",
"link": "/careers",
"taxonomy": "site"
},
"checkplease": {
"name": "KQED food",
"grouping": [
"food",
"bayareabites",
"checkplease"
],
"parent": "food",
"type": "terms",
"id": "checkplease",
"slug": "checkplease",
"link": "/food",
"taxonomy": "site"
},
"education": {
"name": "Education",
"grouping": [
"education"
],
"type": "terms",
"id": "education",
"slug": "education",
"link": "/education",
"taxonomy": "site"
},
"elections": {
"name": "Elections",
"type": "terms",
"id": "elections",
"slug": "elections",
"link": "/elections",
"taxonomy": "site"
},
"events": {
"name": "Events",
"type": "terms",
"id": "events",
"slug": "events",
"link": "/events",
"taxonomy": "site"
},
"event": {
"name": "Event",
"alias": "events",
"type": "terms",
"id": "event",
"slug": "event",
"link": "/event",
"taxonomy": "site"
},
"filmschoolshorts": {
"name": "Film School Shorts",
"type": "terms",
"id": "filmschoolshorts",
"slug": "filmschoolshorts",
"link": "/filmschoolshorts",
"taxonomy": "site"
},
"food": {
"name": "KQED food",
"grouping": [
"food",
"bayareabites",
"checkplease"
],
"type": "terms",
"id": "food",
"slug": "food",
"link": "/food",
"taxonomy": "site"
},
"forum": {
"name": "Forum",
"relatedContentQuery": "posts/forum?",
"parent": "news",
"type": "terms",
"id": "forum",
"slug": "forum",
"link": "/forum",
"taxonomy": "site"
},
"futureofyou": {
"name": "Future of You",
"grouping": [
"science",
"futureofyou"
],
"parent": "science",
"type": "terms",
"id": "futureofyou",
"slug": "futureofyou",
"link": "/futureofyou",
"taxonomy": "site"
},
"jpepinheart": {
"name": "KQED food",
"relatedContentQuery": "posts/food,bayareabites,checkplease",
"parent": "food",
"type": "terms",
"id": "jpepinheart",
"slug": "jpepinheart",
"link": "/food",
"taxonomy": "site"
},
"liveblog": {
"name": "Live Blog",
"type": "terms",
"id": "liveblog",
"slug": "liveblog",
"link": "/liveblog",
"taxonomy": "site"
},
"livetv": {
"name": "Live TV",
"parent": "tv",
"type": "terms",
"id": "livetv",
"slug": "livetv",
"link": "/livetv",
"taxonomy": "site"
},
"lowdown": {
"name": "The Lowdown",
"relatedContentQuery": "posts/lowdown?",
"parent": "news",
"type": "terms",
"id": "lowdown",
"slug": "lowdown",
"link": "/lowdown",
"taxonomy": "site"
},
"mindshift": {
"name": "Mindshift",
"parent": "news",
"description": "MindShift explores the future of education by highlighting the innovative – and sometimes counterintuitive – ways educators and parents are helping all children succeed.",
"type": "terms",
"id": "mindshift",
"slug": "mindshift",
"link": "/mindshift",
"taxonomy": "site"
},
"news": {
"name": "News",
"grouping": [
"news",
"forum"
],
"type": "terms",
"id": "news",
"slug": "news",
"link": "/news",
"taxonomy": "site"
},
"newsletters": {
"name": "newsletters",
"type": "terms",
"id": "newsletters",
"slug": "newsletters",
"link": "/newsletters",
"taxonomy": "site"
},
"perspectives": {
"name": "Perspectives",
"parent": "radio",
"type": "terms",
"id": "perspectives",
"slug": "perspectives",
"link": "/perspectives",
"taxonomy": "site"
},
"podcasts": {
"name": "Podcasts",
"type": "terms",
"id": "podcasts",
"slug": "podcasts",
"link": "/podcasts",
"taxonomy": "site"
},
"pop": {
"name": "Pop",
"parent": "arts",
"type": "terms",
"id": "pop",
"slug": "pop",
"link": "/pop",
"taxonomy": "site"
},
"pressroom": {
"name": "Pressroom",
"type": "terms",
"id": "pressroom",
"slug": "pressroom",
"link": "/pressroom",
"taxonomy": "site"
},
"quest": {
"name": "Quest",
"parent": "science",
"type": "terms",
"id": "quest",
"slug": "quest",
"link": "/quest",
"taxonomy": "site"
},
"radio": {
"name": "Radio",
"grouping": [
"forum",
"perspectives"
],
"description": "Listen to KQED Public Radio – home of Forum and The California Report – on 88.5 FM in San Francisco, 89.3 FM in Sacramento, 88.3 FM in Santa Rosa and 88.1 FM in Martinez.",
"type": "terms",
"id": "radio",
"slug": "radio",
"link": "/radio",
"taxonomy": "site"
},
"root": {
"name": "KQED",
"image": "https://ww2.kqed.org/app/uploads/2020/02/KQED-OG-Image@1x.png",
"imageWidth": 1200,
"imageHeight": 630,
"headData": {
"title": "KQED | News, Radio, Podcasts, TV | Public Media for Northern California",
"description": "KQED provides public radio, television, and independent reporting on issues that matter to the Bay Area. We’re the NPR and PBS member station for Northern California."
},
"type": "terms",
"id": "root",
"slug": "root",
"link": "/root",
"taxonomy": "site"
},
"science": {
"name": "Science",
"grouping": [
"science",
"futureofyou"
],
"description": "KQED Science brings you award-winning science and environment coverage from the Bay Area and beyond.",
"type": "terms",
"id": "science",
"slug": "science",
"link": "/science",
"taxonomy": "site"
},
"stateofhealth": {
"name": "State of Health",
"parent": "science",
"type": "terms",
"id": "stateofhealth",
"slug": "stateofhealth",
"link": "/stateofhealth",
"taxonomy": "site"
},
"support": {
"name": "Support",
"type": "terms",
"id": "support",
"slug": "support",
"link": "/support",
"taxonomy": "site"
},
"thedolist": {
"name": "The Do List",
"parent": "arts",
"type": "terms",
"id": "thedolist",
"slug": "thedolist",
"link": "/thedolist",
"taxonomy": "site"
},
"trulyca": {
"name": "Truly CA",
"grouping": [
"arts",
"pop",
"trulyca"
],
"parent": "arts",
"type": "terms",
"id": "trulyca",
"slug": "trulyca",
"link": "/trulyca",
"taxonomy": "site"
},
"tv": {
"name": "TV",
"type": "terms",
"id": "tv",
"slug": "tv",
"link": "/tv",
"taxonomy": "site"
},
"voterguide": {
"name": "Voter Guide",
"parent": "elections",
"alias": "elections",
"type": "terms",
"id": "voterguide",
"slug": "voterguide",
"link": "/voterguide",
"taxonomy": "site"
},
"guiaelectoral": {
"name": "Guia Electoral",
"parent": "elections",
"alias": "elections",
"type": "terms",
"id": "guiaelectoral",
"slug": "guiaelectoral",
"link": "/guiaelectoral",
"taxonomy": "site"
},
"mindshift_tag_artificial-intelligence": {
"isLoading": true
},
"mindshift_1023": {
"type": "terms",
"id": "mindshift_1023",
"meta": {
"index": "terms_1716263798",
"site": "mindshift",
"id": "1023",
"found": true
},
"relationships": {},
"featImg": null,
"name": "artificial intelligence",
"description": null,
"taxonomy": "tag",
"headData": {
"twImgId": null,
"twTitle": null,
"ogTitle": null,
"ogImgId": null,
"twDescription": null,
"description": null,
"title": "artificial intelligence Archives | KQED Mindshift",
"ogDescription": null
},
"ttid": 1028,
"slug": "artificial-intelligence",
"isLoading": false,
"link": "/mindshift/tag/artificial-intelligence"
},
"mindshift_21847": {
"type": "terms",
"id": "mindshift_21847",
"meta": {
"index": "terms_1716263798",
"site": "mindshift",
"id": "21847",
"found": true
},
"relationships": {},
"featImg": null,
"name": "MindShift",
"description": null,
"taxonomy": "program",
"headData": {
"twImgId": null,
"twTitle": null,
"ogTitle": null,
"ogImgId": null,
"twDescription": null,
"description": null,
"title": "MindShift Archives - KQED Mindshift",
"ogDescription": null
},
"ttid": 21119,
"slug": "mindshift",
"isLoading": false,
"link": "/mindshift/program/mindshift"
},
"mindshift_21504": {
"type": "terms",
"id": "mindshift_21504",
"meta": {
"index": "terms_1716263798",
"site": "mindshift",
"id": "21504",
"found": true
},
"relationships": {},
"featImg": null,
"name": "Education research",
"description": null,
"taxonomy": "category",
"headData": {
"twImgId": null,
"twTitle": null,
"ogTitle": null,
"ogImgId": null,
"twDescription": null,
"description": null,
"title": "Education research Archives | KQED Mindshift",
"ogDescription": null
},
"ttid": 20776,
"slug": "education-research",
"isLoading": false,
"link": "/mindshift/category/education-research"
},
"mindshift_21469": {
"type": "terms",
"id": "mindshift_21469",
"meta": {
"index": "terms_1716263798",
"site": "mindshift",
"id": "21469",
"found": true
},
"relationships": {},
"featImg": null,
"name": "good teaching",
"description": null,
"taxonomy": "tag",
"headData": {
"twImgId": null,
"twTitle": null,
"ogTitle": null,
"ogImgId": null,
"twDescription": null,
"description": null,
"title": "good teaching Archives | KQED Mindshift",
"ogDescription": null
},
"ttid": 20741,
"slug": "good-teaching",
"isLoading": false,
"link": "/mindshift/tag/good-teaching"
},
"mindshift_20616": {
"type": "terms",
"id": "mindshift_20616",
"meta": {
"index": "terms_1716263798",
"site": "mindshift",
"id": "20616",
"found": true
},
"relationships": {},
"featImg": null,
"name": "student engagement",
"description": null,
"taxonomy": "tag",
"headData": {
"twImgId": null,
"twTitle": null,
"ogTitle": null,
"ogImgId": null,
"twDescription": null,
"description": null,
"title": "student engagement Archives | KQED Mindshift",
"ogDescription": null
},
"ttid": 19893,
"slug": "student-engagement",
"isLoading": false,
"link": "/mindshift/tag/student-engagement"
},
"mindshift_963": {
"type": "terms",
"id": "mindshift_963",
"meta": {
"index": "terms_1716263798",
"site": "mindshift",
"id": "963",
"found": true
},
"relationships": {},
"featImg": null,
"name": "teaching with tech",
"description": null,
"taxonomy": "tag",
"headData": {
"twImgId": null,
"twTitle": null,
"ogTitle": null,
"ogImgId": null,
"twDescription": null,
"description": null,
"title": "teaching with tech Archives | KQED Mindshift",
"ogDescription": null
},
"ttid": 968,
"slug": "teaching-with-tech",
"isLoading": false,
"link": "/mindshift/tag/teaching-with-tech"
},
"mindshift_21892": {
"type": "terms",
"id": "mindshift_21892",
"meta": {
"index": "terms_1716263798",
"site": "mindshift",
"id": "21892",
"found": true
},
"relationships": {},
"featImg": null,
"name": "Education",
"description": null,
"taxonomy": "interest",
"headData": {
"twImgId": null,
"twTitle": null,
"ogTitle": null,
"ogImgId": null,
"twDescription": null,
"description": null,
"title": "Education Archives - MindShift",
"ogDescription": null
},
"ttid": 21164,
"slug": "education",
"isLoading": false,
"link": "/mindshift/interest/education"
},
"mindshift_21078": {
"type": "terms",
"id": "mindshift_21078",
"meta": {
"index": "terms_1716263798",
"site": "mindshift",
"id": "21078",
"found": true
},
"relationships": {},
"featImg": null,
"name": "cognitive development",
"description": null,
"taxonomy": "tag",
"headData": {
"twImgId": null,
"twTitle": null,
"ogTitle": null,
"ogImgId": null,
"twDescription": null,
"description": null,
"title": "cognitive development Archives | KQED Mindshift",
"ogDescription": null
},
"ttid": 20350,
"slug": "cognitive-development",
"isLoading": false,
"link": "/mindshift/tag/cognitive-development"
},
"mindshift_843": {
"type": "terms",
"id": "mindshift_843",
"meta": {
"index": "terms_1716263798",
"site": "mindshift",
"id": "843",
"found": true
},
"relationships": {},
"featImg": null,
"name": "critical thinking",
"description": null,
"taxonomy": "tag",
"headData": {
"twImgId": null,
"twTitle": null,
"ogTitle": null,
"ogImgId": null,
"twDescription": null,
"description": null,
"title": "critical thinking Archives | KQED Mindshift",
"ogDescription": null
},
"ttid": 846,
"slug": "critical-thinking",
"isLoading": false,
"link": "/mindshift/tag/critical-thinking"
},
"mindshift_20893": {
"type": "terms",
"id": "mindshift_20893",
"meta": {
"index": "terms_1716263798",
"site": "mindshift",
"id": "20893",
"found": true
},
"relationships": {},
"featImg": null,
"name": "math anxiety",
"description": null,
"taxonomy": "tag",
"headData": {
"twImgId": null,
"twTitle": null,
"ogTitle": null,
"ogImgId": null,
"twDescription": null,
"description": null,
"title": "math anxiety Archives | KQED Mindshift",
"ogDescription": null
},
"ttid": 20171,
"slug": "math-anxiety",
"isLoading": false,
"link": "/mindshift/tag/math-anxiety"
},
"mindshift_21949": {
"type": "terms",
"id": "mindshift_21949",
"meta": {
"index": "terms_1716263798",
"site": "mindshift",
"id": "21949",
"found": true
},
"relationships": {},
"name": "math education",
"slug": "math-education",
"taxonomy": "tag",
"description": null,
"featImg": null,
"headData": {
"title": "math education - KQED Mindshift",
"description": null,
"ogTitle": null,
"ogDescription": null,
"ogImgId": null,
"twTitle": null,
"twDescription": null,
"twImgId": null
},
"ttid": 21221,
"isLoading": false,
"link": "/mindshift/tag/math-education"
},
"mindshift_739": {
"type": "terms",
"id": "mindshift_739",
"meta": {
"index": "terms_1716263798",
"site": "mindshift",
"id": "739",
"found": true
},
"relationships": {},
"featImg": null,
"name": "cheating",
"description": null,
"taxonomy": "tag",
"headData": {
"twImgId": null,
"twTitle": null,
"ogTitle": null,
"ogImgId": null,
"twDescription": null,
"description": null,
"title": "cheating Archives | KQED Mindshift",
"ogDescription": null
},
"ttid": 742,
"slug": "cheating",
"isLoading": false,
"link": "/mindshift/tag/cheating"
},
"mindshift_68": {
"type": "terms",
"id": "mindshift_68",
"meta": {
"index": "terms_1716263798",
"site": "mindshift",
"id": "68",
"found": true
},
"relationships": {},
"featImg": null,
"name": "Higher Education",
"description": null,
"taxonomy": "tag",
"headData": {
"twImgId": null,
"twTitle": null,
"ogTitle": null,
"ogImgId": null,
"twDescription": null,
"description": null,
"title": "Higher Education Archives - KQED Mindshift",
"ogDescription": null
},
"ttid": 68,
"slug": "higher-education",
"isLoading": false,
"link": "/mindshift/tag/higher-education"
},
"mindshift_193": {
"type": "terms",
"id": "mindshift_193",
"meta": {
"index": "terms_1716263798",
"site": "mindshift",
"id": "193",
"found": true
},
"relationships": {},
"featImg": null,
"name": "Teaching Strategies",
"description": "Innovative ideas - projects, processes, curricula, and more - that are transforming how we teach and learn.",
"taxonomy": "category",
"headData": {
"twImgId": null,
"twTitle": null,
"ogTitle": null,
"ogImgId": null,
"twDescription": null,
"description": "Innovative ideas - projects, processes, curricula, and more - that are transforming how we teach and learn.",
"title": "Teaching Strategies Archives | KQED Mindshift",
"ogDescription": null
},
"ttid": 193,
"slug": "teaching-strategies",
"isLoading": false,
"link": "/mindshift/category/teaching-strategies"
},
"mindshift_20934": {
"type": "terms",
"id": "mindshift_20934",
"meta": {
"index": "terms_1716263798",
"site": "mindshift",
"id": "20934",
"found": true
},
"relationships": {},
"featImg": null,
"name": "special education",
"description": null,
"taxonomy": "tag",
"headData": {
"twImgId": null,
"twTitle": null,
"ogTitle": null,
"ogImgId": null,
"twDescription": null,
"description": null,
"title": "special education Archives | KQED Mindshift",
"ogDescription": null
},
"ttid": 20206,
"slug": "special-education",
"isLoading": false,
"link": "/mindshift/tag/special-education"
},
"mindshift_563": {
"type": "terms",
"id": "mindshift_563",
"meta": {
"index": "terms_1716263798",
"site": "mindshift",
"id": "563",
"found": true
},
"relationships": {},
"featImg": null,
"name": "homework",
"description": null,
"taxonomy": "tag",
"headData": {
"twImgId": null,
"twTitle": null,
"ogTitle": null,
"ogImgId": null,
"twDescription": null,
"description": null,
"title": "homework Archives | KQED Mindshift",
"ogDescription": null
},
"ttid": 566,
"slug": "homework",
"isLoading": false,
"link": "/mindshift/tag/homework"
},
"mindshift_20816": {
"type": "terms",
"id": "mindshift_20816",
"meta": {
"index": "terms_1716263798",
"site": "mindshift",
"id": "20816",
"found": true
},
"relationships": {},
"featImg": null,
"name": "screen time",
"description": null,
"taxonomy": "tag",
"headData": {
"twImgId": null,
"twTitle": null,
"ogTitle": null,
"ogImgId": null,
"twDescription": null,
"description": null,
"title": "screen time Archives | KQED Mindshift",
"ogDescription": null
},
"ttid": 20093,
"slug": "screen-time",
"isLoading": false,
"link": "/mindshift/tag/screen-time"
},
"mindshift_21322": {
"type": "terms",
"id": "mindshift_21322",
"meta": {
"index": "terms_1716263798",
"site": "mindshift",
"id": "21322",
"found": true
},
"relationships": {},
"featImg": null,
"name": "antiracist",
"description": null,
"taxonomy": "tag",
"headData": {
"twImgId": null,
"twTitle": null,
"ogTitle": null,
"ogImgId": null,
"twDescription": null,
"description": null,
"title": "antiracist Archives | KQED Mindshift",
"ogDescription": null
},
"ttid": 20594,
"slug": "antiracist",
"isLoading": false,
"link": "/mindshift/tag/antiracist"
},
"mindshift_20818": {
"type": "terms",
"id": "mindshift_20818",
"meta": {
"index": "terms_1716263798",
"site": "mindshift",
"id": "20818",
"found": true
},
"relationships": {},
"featImg": null,
"name": "bias",
"description": null,
"taxonomy": "tag",
"headData": {
"twImgId": null,
"twTitle": null,
"ogTitle": null,
"ogImgId": null,
"twDescription": null,
"description": null,
"title": "bias Archives | KQED Mindshift",
"ogDescription": null
},
"ttid": 20095,
"slug": "bias",
"isLoading": false,
"link": "/mindshift/tag/bias"
},
"mindshift_21304": {
"type": "terms",
"id": "mindshift_21304",
"meta": {
"index": "terms_1716263798",
"site": "mindshift",
"id": "21304",
"found": true
},
"relationships": {},
"featImg": null,
"name": "gender bias",
"description": null,
"taxonomy": "tag",
"headData": {
"twImgId": null,
"twTitle": null,
"ogTitle": null,
"ogImgId": null,
"twDescription": null,
"description": null,
"title": "gender bias Archives | KQED Mindshift",
"ogDescription": null
},
"ttid": 20576,
"slug": "gender-bias",
"isLoading": false,
"link": "/mindshift/tag/gender-bias"
},
"mindshift_21067": {
"type": "terms",
"id": "mindshift_21067",
"meta": {
"index": "terms_1716263798",
"site": "mindshift",
"id": "21067",
"found": true
},
"relationships": {},
"featImg": null,
"name": "media literacy",
"description": null,
"taxonomy": "tag",
"headData": {
"twImgId": null,
"twTitle": null,
"ogTitle": null,
"ogImgId": null,
"twDescription": null,
"description": null,
"title": "media literacy Archives | KQED Mindshift",
"ogDescription": null
},
"ttid": 20339,
"slug": "media-literacy",
"isLoading": false,
"link": "/mindshift/tag/media-literacy"
},
"mindshift_22000": {
"type": "terms",
"id": "mindshift_22000",
"meta": {
"index": "terms_1716263798",
"site": "mindshift",
"id": "22000",
"found": true
},
"relationships": {},
"name": "AI companions",
"slug": "ai-companions",
"taxonomy": "tag",
"description": null,
"featImg": null,
"headData": {
"title": "AI companions - KQED Mindshift",
"description": null,
"ogTitle": null,
"ogDescription": null,
"ogImgId": null,
"twTitle": null,
"twDescription": null,
"twImgId": null
},
"ttid": 21272,
"isLoading": false,
"link": "/mindshift/tag/ai-companions"
},
"mindshift_21511": {
"type": "terms",
"id": "mindshift_21511",
"meta": {
"index": "terms_1716263798",
"site": "mindshift",
"id": "21511",
"found": true
},
"relationships": {},
"featImg": null,
"name": "ChatGPT",
"description": null,
"taxonomy": "tag",
"headData": {
"twImgId": null,
"twTitle": null,
"ogTitle": null,
"ogImgId": null,
"twDescription": null,
"description": null,
"title": "ChatGPT Archives | KQED Mindshift",
"ogDescription": null
},
"ttid": 20783,
"slug": "chatgpt",
"isLoading": false,
"link": "/mindshift/tag/chatgpt"
},
"mindshift_918": {
"type": "terms",
"id": "mindshift_918",
"meta": {
"index": "terms_1716263798",
"site": "mindshift",
"id": "918",
"found": true
},
"relationships": {},
"featImg": null,
"name": "digital writing",
"description": null,
"taxonomy": "tag",
"headData": {
"twImgId": null,
"twTitle": null,
"ogTitle": null,
"ogImgId": null,
"twDescription": null,
"description": null,
"title": "digital writing Archives | KQED Mindshift",
"ogDescription": null
},
"ttid": 922,
"slug": "digital-writing",
"isLoading": false,
"link": "/mindshift/tag/digital-writing"
},
"mindshift_21413": {
"type": "terms",
"id": "mindshift_21413",
"meta": {
"index": "terms_1716263798",
"site": "mindshift",
"id": "21413",
"found": true
},
"relationships": {},
"featImg": null,
"name": "tutoring",
"description": null,
"taxonomy": "tag",
"headData": {
"twImgId": null,
"twTitle": null,
"ogTitle": null,
"ogImgId": null,
"twDescription": null,
"description": null,
"title": "tutoring Archives | KQED Mindshift",
"ogDescription": null
},
"ttid": 20685,
"slug": "tutoring",
"isLoading": false,
"link": "/mindshift/tag/tutoring"
},
"mindshift_21102": {
"type": "terms",
"id": "mindshift_21102",
"meta": {
"index": "terms_1716263798",
"site": "mindshift",
"id": "21102",
"found": true
},
"relationships": {},
"featImg": null,
"name": "Zone of Proximal Development",
"description": null,
"taxonomy": "tag",
"headData": {
"twImgId": null,
"twTitle": null,
"ogTitle": null,
"ogImgId": null,
"twDescription": null,
"description": null,
"title": "Zone of Proximal Development Archives | KQED Mindshift",
"ogDescription": null
},
"ttid": 20374,
"slug": "zone-of-proximal-development",
"isLoading": false,
"link": "/mindshift/tag/zone-of-proximal-development"
},
"mindshift_21584": {
"type": "terms",
"id": "mindshift_21584",
"meta": {
"index": "terms_1716263798",
"site": "mindshift",
"id": "21584",
"found": true
},
"relationships": {},
"featImg": null,
"name": "argument writing",
"description": null,
"taxonomy": "tag",
"headData": {
"twImgId": null,
"twTitle": null,
"ogTitle": null,
"ogImgId": null,
"twDescription": null,
"description": null,
"title": "argument writing Archives - KQED Mindshift",
"ogDescription": null
},
"ttid": 20856,
"slug": "argument-writing",
"isLoading": false,
"link": "/mindshift/tag/argument-writing"
},
"mindshift_862": {
"type": "terms",
"id": "mindshift_862",
"meta": {
"index": "terms_1716263798",
"site": "mindshift",
"id": "862",
"found": true
},
"relationships": {},
"featImg": null,
"name": "creativity",
"description": null,
"taxonomy": "tag",
"headData": {
"twImgId": null,
"twTitle": null,
"ogTitle": null,
"ogImgId": null,
"twDescription": null,
"description": null,
"title": "creativity Archives | KQED Mindshift",
"ogDescription": null
},
"ttid": 865,
"slug": "creativity",
"isLoading": false,
"link": "/mindshift/tag/creativity"
},
"mindshift_22002": {
"type": "terms",
"id": "mindshift_22002",
"meta": {
"index": "terms_1716263798",
"site": "mindshift",
"id": "22002",
"found": true
},
"relationships": {},
"name": "generative ai",
"slug": "generative-ai",
"taxonomy": "tag",
"description": null,
"featImg": null,
"headData": {
"title": "generative ai - KQED Mindshift",
"description": null,
"ogTitle": null,
"ogDescription": null,
"ogImgId": null,
"twTitle": null,
"twDescription": null,
"twImgId": null
},
"ttid": 21274,
"isLoading": false,
"link": "/mindshift/tag/generative-ai"
},
"mindshift_851": {
"type": "terms",
"id": "mindshift_851",
"meta": {
"index": "terms_1716263798",
"site": "mindshift",
"id": "851",
"found": true
},
"relationships": {},
"featImg": null,
"name": "writing",
"description": null,
"taxonomy": "tag",
"headData": {
"twImgId": null,
"twTitle": null,
"ogTitle": null,
"ogImgId": null,
"twDescription": null,
"description": null,
"title": "writing Archives | KQED Mindshift",
"ogDescription": null
},
"ttid": 854,
"slug": "writing",
"isLoading": false,
"link": "/mindshift/tag/writing"
}
},
"userPermissionsReducer": {
"wpLoggedIn": false
},
"eventsReducer": {},
"fssReducer": {},
"tvDailyScheduleReducer": {},
"tvWeeklyScheduleReducer": {},
"tvPrimetimeScheduleReducer": {},
"tvMonthlyScheduleReducer": {},
"userAccountReducer": {
"user": {
"email": null,
"emailStatus": "EMAIL_UNVALIDATED",
"loggedStatus": "LOGGED_OUT",
"loggingChecked": false,
"articles": [],
"firstName": null,
"lastName": null,
"phoneNumber": null,
"fetchingMembership": false,
"membershipError": false,
"memberships": [
{
"id": null,
"startDate": null,
"firstName": null,
"lastName": null,
"familyNumber": null,
"memberNumber": null,
"memberSince": null,
"expirationDate": null,
"pfsEligible": false,
"isSustaining": false,
"membershipLevel": "Prospect",
"membershipStatus": "Non Member",
"lastGiftDate": null,
"renewalDate": null,
"lastDonationAmount": null
}
]
},
"authModal": {
"isOpen": false,
"view": "LANDING_VIEW"
},
"error": null
},
"youthMediaReducer": {},
"checkPleaseReducer": {
"filterData": {
"region": {
"key": "Restaurant Region",
"filters": [
"Any Region"
]
},
"cuisine": {
"key": "Restaurant Cuisine",
"filters": [
"Any Cuisine"
]
}
},
"restaurantDataById": {},
"restaurantIdsSorted": [],
"error": null
},
"userAgentReducer": {
"userAgent": "Mozilla/5.0 AppleWebKit/537.36 (KHTML, like Gecko; compatible; ClaudeBot/1.0; +claudebot@anthropic.com)",
"isBot": true
}
}
