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Anthropic's new Mythos-class AI, Claude Fable 5, compressed two months of human work into a single day for Stripe. This changes everything for AI in education.

In this episode:

• Anthropic's new Mythos-class AI, Claude Fable 5, achieved a 50-million-line codebase migration for Stripe in one day, a task estimated to take humans two months, signifying a major leap for AI in education.
• Effective teaching with AI requires fostering 'task imagination' in students, enabling them to define multi-day projects for AI and articulate clear quality criteria.
• AI assessment for educators should evolve to evaluate students' ability to direct and critically judge AI-generated work, rather than just their capacity to perform tasks themselves.
• Strict safety classifiers on Claude Fable 5, sometimes rerouting science queries, provide valuable, live examples for teaching AI literacy in schools about governance, ethics, and the dual-use dilemma.
• School leaders deploying AI for school operations must carefully examine usage-based pricing models for new AIs like Claude Fable 5 and review data retention policies (e.g., 30-day retention) against data protection obligations.

Chapters:

• 00:00 — Cold open & welcome
• 00:30 — Introducing Claude Fable 5: A Mythos-class AI and its impact on education
• 01:25 — Beyond benchmarks: Fable 5's leap in delegation and responsibility
• 02:30 — The missing skill: Preparing students for 'task imagination' with AI
• 03:45 — Real-world AI literacy: Dual-use dilemma and Fable 5's safety guardrails
• 05:00 — Teaching with AI: Ethics, judgment, and critical thinking with Fable 5
• 06:00 — Nuances for school leaders: Pricing and data retention for AI in education
• 07:30 — The future of AI assessment: Directing and judging work, not just doing it

What is Mythos-class AI and how does it change AI in education?
Mythos-class AI, exemplified by Anthropic's Claude Fable 5, can autonomously manage complex, multi-day projects, requiring educators to prepare students to 'delegate well' and develop 'task imagination' rather than just perform tasks themselves.

How can teachers use AI marking safely with advanced models like Fable 5?
While Fable 5's primary use isn't marking, its underlying principle of delegating responsibilities rather than discrete tasks means teachers should focus on designing comprehensive AI assessment for educators that evaluates students' ability to direct and judge AI work, while remaining vigilant about data retention policies.

What is 'task imagination' and why is it important for AI literacy in schools?
Task imagination is the ability to define a large, multi-day project for an AI, articulate precise quality criteria, and then evaluate its output; this skill is crucial for AI literacy in schools as advanced AIs like Claude Fable 5 demand clear, complex briefs to operate effectively.

Featuring: Dan Fitzpatrick, Anthropic, Claude Fable 5, Opus, Mythos-class, FrontierCode, Stripe, Felix Ryberg, Nate B. Jones.

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The world's first AI-designed vaccine, whose active ingredient was conceived by machine learning, just passed its initial human safety tests.

In this episode:

• The world's first AI-designed vaccine, developed by the University of Cambridge and DIOSynVax, successfully completed initial human safety trials.
• This AI in vaccine development focuses on creating "super antigens" that target stable features across entire viral families, including future threats, moving beyond reactive development.
• The AI designed vaccine uses DNA, making it more stable for global distribution, and can be administered via microfluid jet for easier, widespread deployment.
• The approach highlights how AI can identify unchanging core principles within complex, evolving systems, offering lessons for curriculum design and the future of vaccines.
• While showing promise in a Phase 1 trial published in the Journal of Infection, further research is crucial to determine the AI designed vaccine's long-term efficacy and protection.

Chapters:

• 00:00 — Cold open & welcome
• 00:27 — The first AI-designed vaccine: a foundational breakthrough
• 01:25 — Moving from reactive to proactive AI in vaccine development
• 02:27 — How AI designs 'super antigens' for broad protection
• 03:45 — AI's lessons for identifying core principles in education
• 04:55 — Practical innovations: DNA vaccine stability and microfluid jet delivery
• 06:10 — Phase 1 trial findings and the human-in-the-loop validation
• 07:20 — Future of vaccines: AI's potential beyond coronaviruses
• 08:20 — Balancing groundbreaking innovation with scientific caution

How is this AI designed vaccine different from previous vaccine development?
This new AI designed vaccine, from the University of Cambridge and DIOSynVax, is the first where the active ingredient (antigen) was entirely conceived by machine learning, targeting stable features across whole viral families rather than individual strains.

What are the practical benefits of this new approach to AI in vaccine development?
The AI designed vaccine uses DNA for greater stability, making it easier to store and transport globally, and it can be administered via a microfluid jet, simplifying large-scale vaccination efforts.

What does this AI healthcare innovation mean for future of vaccines?
This AI-driven method aims to create "future-proofed" vaccines that can anticipate and protect against emergent threats like new Sarbeco coronaviruses or seasonal flu, shifting vaccine development from reactive to proactive.

Featuring: Dan Fitzpatrick, University of Cambridge, DIOSynVax, Journal of Infection, Sarbeco coronavirus, Jonathan Heeney, Saul Faust, Marian Knight, NIHR.

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An AI tutor helped students get right answers but not grasp core concepts, highlighting how AI in schools can silence productive struggle and deeper learning.

In this episode:

• An observation of seventh-grade math students showed AI tutors in schools can help students get right answers without truly understanding core concepts like fractions, raising concerns about AI for deeper learning.
• Shael Polakow-Suransky, president of Bank Street College of Education, argues that AI can strip away 'productive struggle,' a crucial element for students to build their own knowledge, emphasizing the human-centered aspect of the AI in education debate.
• Integrating AI into classrooms could deepen social isolation among teens, mirroring concerns raised by Jonathan Haidt about excessive screen time and the need for more student AI interaction.
• The New York Board of Regents' "portrait of a graduate" framework emphasizes critical thinking, communication, and creative problem-solving, underscoring the need for teacher AI tools that support complex, project-based learning.
• Science teacher Brendan Harney discovered students prefer a real teacher for complex problems, using AI to help students probe assumptions *before* human interaction, illustrating a balanced approach to teacher AI tools.

Chapters:

• 00:00 — Cold open & welcome
• 00:45 — The silent classroom: AI tutors helping, but not teaching, fractions
• 01:45 — The cost of silence: Why productive struggle is essential for deeper learning
• 02:45 — AI tutors in schools: Undermining relationships and the Bank Street approach
• 03:45 — Social implications: Jonathan Haidt's warnings on isolation and student AI interaction
• 04:45 — Systemic issues: How standardized testing influences AI deployment and equity
• 05:45 — A path forward: Designing AI for deeper learning and authentic assessment
• 06:45 — Teacher AI tools: Brendan Harney's strategy for human-in-the-loop AI
• 07:45 — The choice: Amplify teachers or replace them with AI tutors in schools

What are the hidden costs of using AI tutors in schools?
The hidden costs include sacrificing 'productive struggle' essential for deep understanding, reducing vital human interaction, and potentially widening educational equity gaps by providing isolated screen time instead of rich, collaborative learning experiences.

How can AI in education support deeper learning without replacing teachers?
AI can support deeper learning by handling logistical tasks, organizing student drafts, and gathering feedback, which frees teachers to focus on critical capacities like ethical debate, complex problem-solving, and fostering genuine student connections.

What is the primary concern about student AI interaction in the classroom?
The primary concern is that over-reliance on one-to-one AI tutors can lead to social isolation, disrupting the relationships and collaborative interactions that are fundamental to how children learn and develop, and which AI cannot replicate.

Featuring: Dan Fitzpatrick, Shael Polakow-Suransky, Bank Street College of Education, Mary Helen Immordino-Yang, Jonathan Haidt, Fannie Lou Hamer Freedom High School, New York Performance Standards Consortium, New York Board of Regents.

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Only four in ten teenagers believe using AI for all homework is cheating, revealing a massive grey area for student perspectives AI.

In this episode:

• A study by Oxford University Press reveals only 44% of students believe using AI for all homework is cheating, highlighting complex student perspectives AI.
• Despite varied views on AI cheating homework, 72% of students prefer not to use AI for school tasks, valuing their own voice and teacher's unique human qualities.
• Students are asking for clear guidance on AI use in schools, with 77% wanting teachers to integrate AI to make complex work easier and offer more one-to-one support.
• Teachers should start AI integration with low-risk tasks and focus on teaching the AI native generation how to critically evaluate AI outputs as 'first drafts.'
• Chris Goodall of Bourne Education Trust points out that if students resort to AI shortcuts, it's often a 'task design problem,' emphasizing the need for pedagogy that encourages deep thinking.

Chapters:

• 00:00 — Cold open & welcome
• 00:30 — Exploring student perspectives AI: The Oxford University Press report
• 01:25 — Only 44% think AI homework is cheating: Understanding student nuance
• 02:30 — Why students hesitate to use AI: Valuing their own voice
• 03:45 — The irreplaceable value of teachers according to students
• 04:30 — What students want from AI: Augmentation, not replacement
• 05:45 — Practical tips for teachers and school leaders to navigate AI in education
• 07:00 — Addressing AI anxiety and the 'first draft' principle
• 07:55 — Rethinking task design to prevent AI cheating homework
• 08:45 — Proactive leadership and a reassuring outlook on the AI native generation

How do student perspectives AI define cheating?
Only 44% of students consider using AI for all homework to be cheating, but nearly one in five think even asking for homework tips from AI is cheating, showing a wide range of understanding.

What do students value most in their teachers regarding AI in education?
Students highly value their teachers' empathy, ability to explain concepts in different ways, and their personality, recognizing these as qualities AI cannot replace.

How can teachers best integrate AI use in schools?
Teachers should start with low-risk tasks like drafting emails, provide specific AI instructions, and treat all AI outputs as 'first drafts,' critically reviewing them with their expertise.

Featuring: Dan Fitzpatrick, Oxford University Press, Teaching the AI Native Generation report, Dr Alexandra Tomescu, Dr Sara Ratner, AI in Education Oxford University (AIEOU), Judith Grey, Oxford’s Educational Research Forum.

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