Education Futures

Daniel Emmerson is the founding Executive Director of the Good Future Foundation, a UK-based charity helping schools around the world adopt AI strategically and responsibly. He is also Academic Affairs Lead at Goodnotes. Before moving into AI and education, he spent 14 years in schools as a teacher and senior leader. Most notably, he was Director of Global Education at Felsted School, where he built cross-cultural learning programmes for more than 4,000 students across 40 countries.

In 2023 he led Goodnotes’ research project “Generative AI in Schools: Evolving Coursework, Homework and Assessment in the Age of AI”, built on conversations with school leaders, teachers, students and support staff worldwide. Its findings led him and Goodnotes founder Steven Chan to launch the Good Future Foundation in April 2024. The charity is fully tech-agnostic and is governed by an advisory council and a student council (aged 15 to 22) with equal oversight. Its flagship AI Quality Mark was co-designed with school leaders including Dr Andy Kemp of the National Mathematics and Science College. It now supports more than 6,000 schools, with partners in South Africa, Ghana, Argentina and South Korea.

Daniel also hosts the Foundation’s podcast, Foundational Impact, sits on the board of the Institute for Global Learning, and serves as a school governor in Essex.

In this episode, Daniel talks with Svenia Busson about:

• From a 2023 Goodnotes research project to a charity — and why it never recommends a single AI tool
• Informed refusal vs. burying your head in the sand: why ignoring AI is the real risk for schools
• The AI Quality Mark’s nine pillars, 1–5 self-assessment and Progress, Bronze, Silver and Gold awards — rewarding the thinking process, not the amount of AI used
• How a Gold school can choose not to use AI for marking after listening to its students — and still score highly
• The “efficiency trap”: why AI rarely saves teachers time — and why starting with purpose matters more
• Beyond AI-detection “whack-a-mole”: marking prompts, AI-use declarations and teachers modelling their own AI use
• A student council’s warning about losing productive friction and the zone of proximal development
• Durable skills for an AI world: perspective-taking, cross-cultural collaboration — and why “a carpenter with AI is also an architect”

Links mentioned

• Good Future Foundation — https://www.goodfuture.foundation
• AI Quality Mark — https://www.goodfuture.foundation/ai-mark
• Foundational Impact podcast — https://www.goodfuture.foundation/podcast
• “Generative AI in Schools: Evolving Coursework, Homework and Assessment in the Age of AI” (Goodnotes, 2023) — https://www.goodnotes.com/research/generative-ai-in-schools
• Institute for Global Learning (formerly the Global Education Benchmark Group) — https://gebg.org
• The Big AI Project (Big Education) — https://bigeducation.org/the-big-ai-project/

Louis de Diesbach is a technology ethicist, author and PhD candidate in Business & Technology Ethics at the Solvay Brussels School of Economics & Management (ULB). He studied Latin and Greek in high school, then management sciences at Solvay, and later went back to earn a Master’s in Ethics and Philosophy at ULB. A mentor, the corporate philosopher Luc de Brabandere, showed him that philosophy and business could live in the same world.

After about ten years in consulting, including at Boston Consulting Group, Louis now works on AI ethics and governance in the corporate world. He co-founded the ULB Research Lab in Business & Technology Ethics, lectures on technology ethics at HEC Liège, and sits on the ethics committee of the computer-vision company XXII. He has written three books: Liker sa servitude (FYP, 2023), Bonjour ChatGPT (Mardaga, 2024, with a foreword by Luc de Brabandere) and Faussaires algorithmiques: l’IA va-t-elle remplacer les artistes ? (Éditions de l’Aube, 2026), which draws on more than 120 conversations with artists, philosophers and lawyers. He writes columns for L’Écho and La Libre Belgique and gave the TEDxNantes talk “Osons penser une nouvelle utopie numérique”.

In this episode, Louis talks with Svenia Busson about:

• Why tech companies need ethicists, and the “ethics washing” behind 1,000+ job openings at OpenAI, Anthropic and Google, none of which required a philosophy degree
• Superintelligence talk as a distraction from AI’s real costs: energy use, scraped creative work, and the hidden labour of data annotators
• The cognitive-debt paradox: executives who discuss AI’s risks in the morning, then have ChatGPT do the afternoon exercise
• Camus’s 1957 letter to his teacher Louis Germain, and why replacing teachers with AI misunderstands what teaching is
• Where AI does help (dyslexia and dyscalculia support, mixed-level classrooms, drafting test questions) and the case for small, specialised models over all-purpose LLMs
• The Max Planck study showing people now say “delve” more often, and what the homogenisation of language means for students still finding their voice
• Six lenses school leaders should apply before adopting AI, with training as the foundation, and the three Cs students need: critical thinking, collaboration and creativity
• Screens, young kids and AI companions, from Her and Black Mirror to Simone de Beauvoir’s question: what will we do with all the time we save?

Dana Suskind is Professor of Surgery and Pediatrics at the University of Chicago and a pediatric cochlear implant surgeon. She earned her BS and MD at the University of Missouri–Kansas City and joined UChicago in 2002, where she founded the Pediatric Hearing Loss and Cochlear Implant Program.

Her patients sent her in a new direction. Two children could get identical implants and still have wildly different outcomes, and she saw that the difference was the language around each child, not the device. She left the operating room part-time and founded the Thirty Million Words Initiative in 2010. In 2017 she co-founded the TMW Center for Early Learning + Public Health with economist John List. The center supports parents and caregivers with programs on the power of talk and interaction, and argues for the policies families need. She wrote Thirty Million Words (2015) and Parent Nation (2022), an instant New York Times bestseller and Nautilus Gold winner. Her new book, Human Raised: Nurturing Connection, Curiosity & Lifelong Learning in the Age of AI (Dutton, 2026), asks what it takes to raise children well now that machines can mimic the interactions that build their brains.

In this episode, Dana talks with Svenia Busson about:

• Two children, identical cochlear implants, opposite outcomes — and what that taught a surgeon about early language environments
• The “helpless infant paradox” and brain-to-brain synchrony — why human connection is biological, not just a verbal back-and-forth (drawing on Ruth Feldman’s research)
• “Good enough” parenting — how friction, missteps, rupture and repair build empathy, theory of mind and frustration tolerance
• The HOPE framework: Human connection, Own your imperfections, Protect the early years (and learning), Enhance not replace
• Sycophantic, frictionless AI and children who learn that relationships revolve around them — “no bots for tots,” versus science-led socially assistive robots and robotic reading companions
• The DETECT method for judging any AI product for children — and a “Developmentally Aligned” seal modelled on the Pure Food and Drug Act and the Good Housekeeping Seal
• The inequality trap — human connection as “the new organic,” and why a low-tech childhood is the best preparation for a high-tech future
• Rethinking what schools are for — human-edge skills, and investing in the parks, museums and civic spaces that bring people together

A surgeon’s case for keeping childhood human while the machines get smarter.

Resources mentioned

• Human Raised, Dana Suskind (Dutton, 2026)
• Parent Nation, Dana Suskind with Lydia Denworth
• TMW Center for Early Learning + Public Health, University of Chicago
• American Academy of Pediatrics, “Digital Ecosystems, Children, and Adolescents” (2026)
• Common Sense Media, AI Toys Risk Assessment (Jan 2026)
• Sarah Sebo’s lab (UChicago), robotic reading companions and reading anxiety
• Brian Scassellati (Yale), social robots and autism
• Maja Matarić (USC), socially assistive robotics

Dr. Sven Schütt is CEO of IU Group, home to IU International University of Applied Sciences, the largest university in Germany. He trained as a neuroscientist, studying physics and philosophy in parallel and finishing both in the standard time. He then earned a PhD on learning at the Max Planck Institute of Neurobiology during the “AI winter.”

After McKinsey, he became Managing Director at Germany’s Federal Employment Agency, where he helped implement the Hartz labour-market reforms, one of Europe’s largest public-sector reforms. He then ran the Hertie School in Berlin. Since 2010 he has turned IU from a 1,500-student hotel-management school into a group with about 130,000 students in Germany and 20,000 more worldwide, with institutions in the UK, Canada and Austria. More than 70% of IU students come from non-academic households, and its dual-study model has 15,000 corporate partners. IU was the first university in the world to give every course an AI companion built on GPT-4. That tool has since grown into Syntea, a multi-agent learning companion.

In this episode, Sven talks with Svenia Busson about:

• Building a university around working adults: start any day, sit an exam any hour — and why access is the core mission
• Inside Syntea: from a Q&A chatbot to a companion that maps your goals, books your exams and nudges you back on WhatsApp
• What IU’s data shows: 58% usage in a 77,000-student study, about 25% fewer early dropouts, about 30% faster learning — and how close that is to Bloom’s “two sigma” effect
• If AI cuts learning time by a third, should a three-year bachelor’s shrink to 18 months? — and why regulation hasn’t caught up
• “From grading to guiding” and cognitive sovereignty — why universities teach judgment, not knowledge (Samuel Johnson declared them obsolete in 1766)
• The AI gender gap: men use AI about 30% more than women — and how organisational encouragement nearly closes it, as AI fluency enters more than 3,000 courses
• The crisis of assessment: proctored colloquia, personalised AI-driven exams and AI-supported grading for fairness
• The future of teaching: the end of passive lectures, “augmented intelligence,” and why research may be disrupted more than teaching

A university CEO arguing that every institution must now become a tech company.

Links & resources mentioned

• IU International University of Applied Sciences: https://www.iu.org
• Syntea, IU’s AI learning companion: https://www.edtechinnovationhub.com/news/iu-expands-syntea-into-ai-learning-companion-for-80000-distance-students
• IU’s 77,000-student analysis of AI learning-assistant use (AIET 2026): https://www.iu.de/news/en/worlds-largest-analysis-of-ai-use-in-higher-education-shows-ai-supported-learning-has-become-part-of-everyday-student-life/
• University Experience Study 2026 (AI fluency of IU students): https://www.iu.de/news/en/study-on-ai-in-higher-education-students-learn-faster-and-more-efficiently/
• IU Job Readiness Framework: https://www.fernstudi.net/news/iu-hochschule-startet-flaechendeckendes-ki-kompetenzmodell-in-der-lehre
• IU AI Research Institute (Prof. Thorsten Fröhlich, Prof. Kamal Bhattacharya): https://www.iu.de/news/en/iu-international-university-of-applied-sciences-establishes-new-ai-research-institute/
• Benjamin Bloom, “The 2 Sigma Problem” (1984): https://journals.sagepub.com/doi/10.3102/0013189X013006004
• Sven Schütt on LinkedIn: https://www.linkedin.com/in/sven-sch%C3%BCtt-04a24140/

Isabelle C. Hau is the Executive Director of the Stanford Accelerator for Learning. It’s a Stanford-wide effort hosted at the Graduate School of Education, and it brings together more than 100 faculty affiliates to connect learning science with practice and policy. She grew up in rural southern France and studied at Sciences Po Paris and ESSEC before earning her MBA at Harvard Business School. She began her career as a financier at Morgan Stanley.

Before Stanford, she spent about a decade with the Omidyar Group. She founded and led the US Education initiative at Omidyar Network and was a founding partner of its spinout, Imaginable Futures, where she backed early-stage organizations including Khan Academy Kids, Ellevation and Handshake. She wrote Love to Learn: The Transformative Power of Care and Connection in Early Education (PublicAffairs, 2025) and the Stanford Social Innovation Review cover story “Welcome to the Era of Relational Intelligence.” She also serves on the Brookings Institution’s Global AI Taskforce and the EDSAFE AI Alliance steering committee, and she writes the weekly newsletter Small Talks.

In this episode, Isabelle talks with Svenia Busson about:

• Relational intelligence (RQ) as the next stage of human intelligence after IQ and EQ, and why we need to decide on purpose what our humanity should become
• The “relational recession”: fewer siblings, fewer friends, less play and less contact between generations, and how AI companionship is moving into that gap
• AI sycophancy: new Stanford and Oxford research on how chatbots that constantly agree with us make real human relationships feel harder and less rewarding
• An 8-workshop pilot at Design Tech High on how to build relationships, including two sessions on conflict and repair
• AI literacy through tinkering instead of a fixed curriculum: the Stanford AI Tinkery and its new free network of schools
• Education’s “Turing Trap” and what the latest PISA results show: AI without scaffolding hurts outcomes, while AI with good teaching support helps
• Where AI is showing real promise: comic-book science projects that made students more curious, an autism screening game that is 86% accurate, and Tutor CoPilot, which brings novice tutors up to the level of the best ones
• Why AI-only tutors struggle to get used, how to measure relationships without reducing them to a score, and what K-12 can learn from medical education

Resources mentioned

Isabelle’s writing

• Hau, I. C. (2025). Love to Learn: The Transformative Power of Care and Connection in Early Education. PublicAffairs. Link
• Hau, I. C. (2026). “Welcome to the Era of Relational Intelligence.” Stanford Social Innovation Review, 24(2), 26–35. Link
• Hau, I. C. (2026). “Why Human Connection Must Be at the Heart of Education in the AI Age.” World Economic Forum. Link
• Hau, I. C., & Schwartz, D. L. (2025/2026). “Avoiding Education’s Turing Trap.” Stanford Social Innovation Review. Link
• Hau, I. C. (2026). “Curiosity, Connection, and the Future of Learning.” Educational Leadership, 83(7). Link

Research on AI sycophancy and relationships

• Cheng, M., et al. (2026). “Sycophantic AI decreases prosocial intentions and promotes dependence.” Science, 391(6792). Link
• Ibrahim, L., et al. (2026). “Sycophantic AI makes human interaction feel more effortful and less satisfying over time.” arXiv:2605.07912. Link
• Kim, P., Xie, Y., & Yang, S. (2025). “‘I am here for you’: How relational conversational AI appeals to adolescents, especially those who are socially and emotionally vulnerable.” arXiv:2512.15117. Link

Research on AI tutoring

• Turano, C., Pihl, V., Agnew, C., Ziegler, L., & Loeb, S. (2026). AI Tutoring Is Not a Monolith: What We Actually Know. SCALE Initiative, Stanford Accelerator for Learning. Link
• Tutor CoPilot, Stanford National Student Support Accelerator. Link

Stanford initiatives

• Stanford Accelerator for Learning. Link
• Stanford AI Tinkery. Link

Alison "Ali" Gellett leads The Big AI Project at the Oasis Big Education Institute. The project aims to give every school in the UK and the Republic of Ireland what it needs to approach AI safely, ethically and strategically. Ali has 30 years of experience in educational leadership across teaching, multi-academy trust (MAT) executive leadership and local government. She is a Fellow of the Royal Society of Arts.

For the past decade, Ali has focused on transforming the education sector by co-designing solutions with schools. She founded and runs the My Future programme, which teaches careers, aspirations and essential skills to primary school children. Since 2021 it has reached more than 35,000 children and enabled over 800,000 encounters between children and employers. It is funded by Essex County Council and has just launched its first London pilot. The Big AI Project grew out of Big Education's Rethinking School programme and is funded by Salesforce. It is free and AI-agnostic, and its materials were designed by teachers and quality-assured by Professor Rose Luckin's team at Educate Ventures Research. After a pilot with more than 230 schools, it is now rolling out nationally. That includes a region-wide partnership with the Liverpool City Region Combined Authority covering its 400–500 schools. Ali co-authored The Big AI Project report (March 2026).

In this episode, Ali talks with Svenia Busson about:

• Why a school's AI strategy should start with vision, purpose and pedagogy, not with choosing tools
• Building a coalition of 50+ partners around shared values, and keeping commercial and political motives out of the room
• A four-hour, AI-agnostic training for heads — and why their confidence rises from 1–2 out of 5 to 4–5 out of 5
• AI literacy from age 3 to 14, with prediction games, bias and stereotyping activities, and class agreements not to share misinformation
• Anthropomorphism, deepfakes and the AI tool that erased the features of a child with Down's syndrome from a nursery resource
• "Truth has gone": misinformation, young people's mental health, and a million 16–25-year-olds not in education, employment or training
• Why human connection and essential skills matter more than ever in a world full of AI
• The Good Future Foundation AI Quality Mark, and why schools that go it alone end up making their job harder

Dr. Ola Ozernov-Palchik is a cognitive neuroscientist who studies how the brain learns to read and, more and more, how AI should (and shouldn't) be used to help it. She is a Research Scientist at MIT's McGovern Institute for Brain Research, a Research Assistant Professor at Boston University Wheelock College of Education & Human Development, and Associate Director of Translational Research in BU's AI & Education Initiative. She got her start in research almost by accident: as an international psychology student, she landed her first research job at the Institute for Reading Research at Southern Methodist University because she spoke Russian.

That job led her to cognitive neuroscience, a PhD in Cognitive Science at Tufts, and early work with Dr. Nadine Gaab at Boston Children's Hospital on some of the first studies to predict dyslexia from kindergarten, followed by a postdoc with John Gabrieli at MIT. She has published 30+ peer-reviewed papers, including a recent Nature Communications study showing the brain's language network is already adult-like by age 4. She received the International Dyslexia Association's Early Career Award, and she is President of New England Research on Dyslexia and Associate Editor of Scientific Studies of Reading.

She founded and directs the Evidence-Based AI in Learning (EVAL) Collaborative, a partnership between BU Wheelock and the Hariri Institute for Computing. EVAL holds AI learning tools to the evidence standard we'd expect of a medical intervention. Its first AI in Learning Challenge drew 35 companies and chose Amira Learning for a free, independent randomized controlled trial. She also co-built KIVA, an open-source AI reading companion that won a 2026 Tools Competition award.

In this episode, Ola talks with Svenia Busson about:

• How the brain learns to read: repurposing circuits for vision and language, the "visual word form area," and why b and d are so hard to tell apart
• What causes dyslexia: phonological processing, family history, and why reading ability sits on a continuum
• Why decades of reading science still don't reach classrooms, with the US as a case study: only ~40% of fourth graders read proficiently on NAEP
• Why no one checks edtech the way medicines are checked: schools buy tools based on user numbers rather than learning outcomes, and some districts are now banning AI outright
• Inside the EVAL Collaborative: pre-registered, open-science RCTs published regardless of outcome, and the first EVAL AI in Learning Challenge
• Can an 8-month trial prove learning? Measurement models, gated technical reviews (including how well speech recognition handles accents and dialects), and evidence that builds up over time
• Advice for edtech founders: collect evidence from day one, and edtech's "5% problem"
• KIVA, skeptical first graders, and the "AI penalty": teachers rated AI tutoring responses higher than human ones until they were told they came from AI

Dr. Nicholas "Nick" Zufelt is Instructor in Mathematics, Statistics, and Computer Science at Phillips Academy Andover in Massachusetts. He was recently appointed Resident Scholar in AI and Education at the school's Tang Institute. He trained as a mathematician, earning a graduate degree and completing a postdoc, then decided research wasn't his path and chose the classroom over a data-science career in tech.

Nick taught himself most of his computer science and machine learning. He now teaches Andover's advanced Machine Learning course, where students build neurons from scratch before touching any library. The day ChatGPT launched, he put it in front of his students and asked, "What do we do with this?" He has been working out the answer with them ever since. As a Tang Fellow, he co-led the ethi{CS} project, which builds ethical deliberation into STEM courses. He is also a founding member and editor of explorations at AI Co-Lab, the teacher-led, peer-to-peer AI professional development network co-founded by Maureen Russo Rodríguez and Nate Green, which has grown to thousands of educators.

In this episode, Nick talks with Svenia Busson about:

• Why computer science still matters when AI writes the code — programming vs. computer science as a liberal art
• Explaining AI to a tenth grader: "code that makes more human-like decisions" — and why "AI" is partly a buzzword
• Metacognition as the core AI skill — the "barometer" (measurement) and the "navigator" (control)
• When and how younger students should meet AI — shared-screen, whole-class use before individual access
• "Practice outside, perform inside, create in both" — a framework for assessment in the age of AI, including oral exams and assessments tightly coupled to learning objectives
• Why teachers shouldn't hand drafting over to AI — lesson plans, parent emails, and protecting your own voice
• Inside the AI Co-Lab model — monthly explorations, intermediary artifacts, and teacher-to-teacher learning
• Two quick wins for teachers: "name the verb" the AI is doing, and the "right-hand side of the chat" reflection prompt

Merlia Shaukath is the Founder and CEO of Madhi Foundation, a Chennai-based nonprofit working to end the foundational learning crisis in India's public schools. She studied Economics at Stella Maris College in Chennai, then earned an MSc in Area Development Studies from the University of Oxford and an MSc in Governance and Management from the London School of Economics. Her grandfather was a social reformer who co-founded educational institutions for India's Muslim community. Her parents were first-generation entrepreneurs.

She was the first staff member at Teach For India Chennai, where she helped build the Chennai chapter. She then moved into policy consulting at Athena Infonomics, working directly with state governments. She founded Madhi in 2016 to close the gap between policy and classroom practice. Madhi is the chief management partner of Tamil Nadu's Ennum Ezhuthum foundational learning mission, which reaches about 37,000 primary schools, 2–2.5 million children and around 100,000 teachers. During that work, the share of classrooms getting the target number of teacher-coach visits rose from 1% to 86%, and Tamil Nadu moved up the National Achievement Survey rankings for the first time in a decade. Madhi is now expanding to Meghalaya and Nagaland. Merlia is an Ashoka Fellow, a Mulago Rainer Arnhold Fellow and an Acumen India Fellow, and a Principal Investigator with the What Works Hub for Global Education at Oxford.

In this episode, Merlia talks with Svenia Busson about:

• Why "no one is self-made," and how a social-reformer grandfather and entrepreneur parents shaped a social entrepreneur
• Going from Teach For India classrooms to state-level policy work, and seeing inequity as a systems problem
• Ten years of systems reform: "scale is humbling," scaling programmes back from great to good, and why there's no playbook for implementation
• Why evidence doesn't automatically lead to change, with the politics of multigrade classrooms (70% of Indian primary schools) as the example
• AI in systems where children can't yet read, and why foundational learning has to mean more than literacy and numeracy in an AI-native world
• AI for the adults in the system: lightening the load for teachers and the 3,500 teacher coaches in Tamil Nadu without adding to cognitive decline
• The numbers she cares about most: coach visits rising from 1% to 86%, and "effective teacher" ratings dropping from 90% to 19% as coaches set a higher bar
• Speech-based assessment and oracy, including why Madhi is collecting voice data from 35,000 children to build a Tamil reading-fluency model, and the emotional cost of AI that gets a child wrong
• Why parents are the overlooked lever: 72% didn't know their child was struggling to read, and what non-literate parents can still do at home

Dr Malak Sadek is Course Director and Teaching Associate at the Centre for Human-Inspired Artificial Intelligence (CHIA) at the University of Cambridge, where she teaches the Conversational AI module of the MPhil in Human-Inspired AI. She holds a PhD in Design Engineering from Imperial College London's Dyson School of Design Engineering. Her thesis, Designing Responsible AI: Integrating Human Values into AI Design, received the school's 2026 PhD Outstanding Thesis Award.

Trained as a computer engineer (BSc, American University in Cairo) and in human-computer interaction (MSc, University of St Andrews), Malak created the Value-Sensitive Conversational Agent (VSCA) Framework and Toolkit for co-designing chatbots with the communities they serve. She also co-authored a review of 52 co-designed conversational agent studies (Design Studies, 2023). Her recent paper, Using Anti-Values as Conversational AI Design Constraints (ACM CUI 2026), asks what AI should never do.

She now directs the HIVE CAI Lab at CHIA. The lab studies how people experience values in conversational AI and how they can steer them. Her research is on adults, not children: how adults use chatbots, rely on them and are shaped by them. She is not a child-development specialist. She has worked with The Alan Turing Institute and the Leverhulme Centre for the Future of Intelligence, and was elected Communications Officer of the British Computer Society's Interaction Specialist Group.

In this episode, Malak talks with Svenia Busson about:

• Why bias, accented voice assistants and "secretary" chatbots all come from the same gap between builders and users
• Anti-values: deciding what AI should not optimise for, and not just what it should do
• Training future AI builders to wear "both hats": cutting-edge engineering plus psychological, environmental and cultural impact
• Co-designing chatbots with communities, with education among the most common use cases in her review of co-design studies
• Her reaction, as a researcher of adult–AI interaction, to parents choosing the sycophantic chatbot in Pilyoung Kim's study (for child-friendly guardrails, see Everyone.AI)
• Should adults form relationships with AI companions? Personalised guardrails that turn empathy down when a user starts becoming over-reliant
• AI value alignment and our "first-order vs. second-order selves": do people know what's good for them?
• Value sliders in chatbot interfaces, convenience culture, and rethinking assessment with AI declaration forms