What AI is doing to a generation of disengaged kids

What AI is doing to a generation of disengaged kids

Jenny Anderson is an award-winning journalist and author with 25 years in the field, including a decade covering finance at The New York Times — where she won a Gerald Loeb Award in 2008 for her coverage of Merrill Lynch ahead of the 2008 financial crisis. She later pioneered coverage of the "science of learning" at Quartz, and now writes on education for TIME and contributes to The New York Times, The Atlantic, and The Washington Post. She is the co-author, with Rebecca Winthrop (Director of the Center for Universal Education at Brookings [https://www.brookings.edu/articles/the-disengagement-gap/]), of The Disengaged Teen: Helping Kids Learn Better, Feel Better, and Live Better (thedisengagedteen.com [https://www.thedisengagedteen.com/]) — the product of five years of research, including a survey of more than 65,000 students and 2,000 parents [https://www.brookings.edu/articles/the-disengagement-gap/] conducted with Brookings and Transcend, into why so many children lose their love of learning in adolescence. She writes the weekly Substack newsletter How to Be Brave [https://howtobebrave.substack.com/], reaching tens of thousands of educators and parents, and hosts The Learnit Podcast. She is also a Fellow with the Learning Sciences Exchange at New America. In this episode, Jenny talks with Svenia Busson about: * The disengagement gap — why 75% of kids love school in primary years, but only 25% still do by 10th grade * The Four Modes framework — Passenger, Achiever, Resistor, and Explorer — and why fewer than 4% of students land in Explorer mode * Why phones and AI aren't the root cause of the teen mental-health crisis — academic pressure consistently ranks higher * School models built for agency — Big Picture Learning [https://www.bigpicture.org/]'s semester-long internships, Red Bridge School [https://www.redbridgesf.org/], and Valor Collegiate Academies [https://valorcollegiate.org/]' "school within a school" * Assessment in the age of AI — competency-based learning, portraits of a graduate, and why parents resist abandoning high-stakes exams like the GCSEs * The AI silence at home — why most teens use AI regularly while few talk to their parents about it [https://www.commonsensemedia.org/research/the-dawn-of-the-ai-era-teens-parents-and-the-adoption-of-generative-ai-at-home-and-school] * AI, writing, and "cognitive stunting" — what outsourcing the first draft costs a developing thinker, building on Rebecca Winthrop's NYT piece [https://www.nytimes.com/2026/05/27/opinion/writing-creativity-ai.html] on AI and creativity * What parents can actually do — testing the tools themselves, and protecting space for productive struggle A clear-eyed guide to why kids disengage — and how to bring them back.

What "no tech sundays" can teach us about AI

Bethany Koby-Hirschmann is a designer, social entrepreneur, and co-founder and Chief Vision Officer of Fam Studio [https://famstudio.co], a research and design practice based in Somerset, England. She holds a BA from the Rhode Island School of Design and an MSc in Responsibility and Business Practice from the University of Bath, and is completing a PhD on youth co-creation and the uses of enchantment. In 2012, after finding a discarded laptop in a skip near her home in East London, she co-founded Tech Will Save Us [https://en.wikipedia.org/wiki/Technology_Will_Save_Us] with her husband, Daniel Hirschmann, on the conviction that children should be producers of technology, not just consumers of it. Tech Will Save Us grew into a STEAM company selling in 97 countries and partnered with the BBC, Microsoft, Samsung, and ARM to design the BBC micro:bit [https://microbit.org] — a pocket-sized computer distributed free to a million UK children that has since reached more than four million users worldwide. After selling the company in 2021, Bethany founded Fam Studio, which co-creates with families and children to build technologies, learning content, and experiences centered on people and the planet. Current projects include a multimodal "Imagination Tool" that uses generative AI to bring children's voices into large-scale co-creation, and a wellbeing-and-AI research partnership with Oxford's Reuben College [https://reuben.ox.ac.uk/artificial-intelligence-and-machine-learning] and its Child-Centred AI Design Lab [https://oxfordccai.org]. In this episode, Bethany talks with Svenia Busson about: * Finding a laptop in a skip — the origin story behind Tech Will Save Us * Designing the BBC micro:bit with Microsoft, Samsung, and ARM — reaching four million children across 97 countries * Why Fam Studio exists to serve "the village," not just the child * Techno-optimism versus AI anxiety — holding both at once * Whether AI reinforces the industrial-era school model or finally breaks it open * Building the "Imagination Tool" — using generative AI to bring children's voices into co-creation at scale * "No Tech Sundays" and the house rules her family set for her teenager's AI use * Moving from human-centered to life-centered design — what biomimicry teaches educators She closes with future-guest picks: Caroline Essame, author of Why Nature Matters (Routledge [https://www.routledge.com/Why-Nature-Matters-Supporting-Childrens-Learning-and-Wellbeing-Through-Nature/Essame/p/book/9781032899206]); Noan Fesnoux, creative adviser to Dubai's Museum of the Future (LinkedIn [https://www.linkedin.com/in/noanfesnoux/]); Liz Robinson, CEO of Big Education (bigeducation.org [https://bigeducation.org/]); and Jenny Gibson of Cambridge's PEDAL Centre (pedalhub.net [https://www.pedalhub.net/]).

Education Futures Live: AI & Education Meetup (London)

This special episode is a recording of a live panel discussion from the AI & Education Meetup series, hosted by Education Futures at the London Interdisciplinary School (LIS). Svenia is joined by four guests at the intersection of AI and learning: Ash Brockwell, LIS associate professor and lead of the new Education Futures master's; Niccolò Pescetelli, LIS associate professor leading the AI & Collective Intelligence program; Stephen Jull, GeoGebra GmbH co-founder now Global Head of AI Educational Technology at Teach For All; and Bibi Groot, behavioral scientist and Chief Impact Officer at Eedi, who runs large-scale RCTs on AI tutors. The conversation moves through what it means to "learn" when AI can retrieve and summarize anything instantly, the risk of cognitive offloading and "cognitive surrender," and evidence from a 12-week chess RCT on hints and productive struggle. The panel also digs into relationality and embodied learning, how Teach For All is building AI literacy at scale across 63 countries, tools designed to cut screen time while still using AI (like Eedi's QR-code diagnostic system), and closes with each guest's vision of a preferable — not just probable — future for education, including a sharp reminder from the audience that 70% of children globally still lack basic literacy access. Recorded live in London as part of Education Futures' ongoing meetup series exploring desirable futures for education in the age of AI.

Why we can't teach AI Literacy yet

We asked one of the most respected education technology researchers in the world a simple question: how should schools teach students to use AI? His answer? We don't know yet, and pretending we do is the problem. Justin Reich is an Associate Professor at MIT and Director of the MIT Teaching Systems Lab [https://tsl.mit.edu], author of Failure to Disrupt [https://tsl.mit.edu/books/failure-to-disrupt/] (Harvard University Press), host of the TeachLab podcast, and the force behind The Homework Machine — a landmark 7-part podcast series investigating what's really happening with AI in classrooms across the US. In this conversation with Svenia Busson, Justin explores: * Why "AI literacy" follows the same broken playbook as digital citizenship and computational thinking — and will likely fail students the same way * What the history of web literacy teaches us: it took 25 years to find strategies that actually work * Why domain expertise — not AI knowledge — may be the most critical factor in using AI well * What to make of "AI-powered" schools like Alpha School * What students themselves are saying: two-thirds of US students say AI is harming their critical thinking * Why "the homework machine" is the most honest name for what's happening in classrooms today Also mentioned in this episode: * Mike Caulfield's SIFT framework for teaching web literacy: https://guides.lib.uchicago.edu/c.php?g=1241077&p=9082322 * A Guide to AI in Schools: Perspectives for the Perplexed — MIT Teaching Systems Lab guidebook (August 2025) https://tsl.mit.edu/wp-content/uploads/2025/08/GuideToAIInSchools.pdf * "Stop Pretending to Know How to Teach AI" — Justin's article in the Chronicle of Higher Education (November 2025) https://www.chronicle.com/article/stop-pretending-you-know-how-to-teach-ai * The Homework Machine podcast: https://podcasts.apple.com/us/podcast/the-homework-machine-what-ai-is-really-doing-in-classrooms/id583456652?i=1000747231954 (a must listen)

Measuring the real impact of AI in education

What does it actually take to know if an AI tutor is helping kids learn? Bibi Groot [https://www.linkedin.com/in/bibi-groot/], Chief Impact Officer at Eedi Labs [https://eedi.com/], has spent her career answering exactly that question — first at the Behavioral Insights Team (aka the Nudge Unit, co-founded with Nobel laureate Richard Thaler), then in classrooms across the UK and Latin America. In this episode, Bibi walks us through how Eedi's diagnostic engine works — 60,000 carefully designed multiple-choice questions, each distractor linked to a specific misconception — and why understanding why a student gets something wrong matters as much as knowing they got it wrong. Bibi also introduces a concept that should alarm everyone in edtech: cognitive surrender — the risk that when AI does all the thinking, students stop learning altogether. Her solution is architectural: don't ask students to self-regulate, build the constraints directly into the system. She references a striking study by Poulidis and Bastani on chess students — those who received AI hints at system-chosen moments improved 64% vs. only 30% for those who could ask for help whenever they wanted. This is a rare, rigorously evidence-based conversation about what responsible AI tutoring actually looks like — and how far most of the field still has to go. References mentioned in this episode: * Behavioral Insights Team (the Nudge Unit) [https://www.bi.team/] * Eedi Labs [https://eedi.com/] — including the free Eedi School platform [https://www.eedischool.com/us] * Google DeepMind's LearnLM [https://cloud.google.com/solutions/learnlm] * Learning Engineering Virtual Institute (LEVI) [https://learning-engineering-virtual-institute.org/] — created by Schmidt Futures & Renaissance Philanthropy * David Yeager, [https://casbs.stanford.edu/10-25-science-motivating-young-people-groundbreaking-approach-leading-next-generation-and-making]10 to 25: The Science of Motivating Young People [https://casbs.stanford.edu/10-25-science-motivating-young-people-groundbreaking-approach-leading-next-generation-and-making] * AI Hub for Education [https://scale.stanford.edu/research-in-action/understanding-evidence-base-ai-k12-education] (Stanford) — reviewed 800+ papers on AI in education; only 20 had causal evidence * Poulidis & Bastani chess study [https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5128584](system-chosen AI hints → 64% improvement vs. 30% for on-demand help) * London EdTech Week [https://www.londonedtechweek.com/] — Meet Bibi & Svenia at the London AI & Education Meetup on June 18, 2026