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Students have mixed feelings about trusting AI decisions in the classroom, but teachers overestimate student trust in AI systems.

In this episode:

• A "trust gap" exists in K-12 AI education: students trust human teachers over AI, while teachers fear students will trust AI more than them, impacting AI trust K-12.
• Students and teachers both highlight AI's inability to understand social dynamics and emotional aspects crucial for group work and learning in the classroom.
• Concerns about AI monitoring causing pressure and data privacy are high among students, who want control over data sharing, primarily with their teachers.
• Students desire autonomy in AI-assisted learning but acknowledge their metacognitive blind spots, often seeking human teacher guidance to avoid easy options.
• Insights from researchers like Niklas Scholz and Martina Vincoli emphasize that AI in education Germany must scaffold student metacognition and build trust through transparency, not just technology deployment.

Chapters:

• 00:00 — Cold open & welcome
• 00:30 — Mind the Trust Gap: Research overview with Tomohiro Nagashima and team
• 01:15 — How Intelligent Tutoring Systems (ITS) were explored
• 01:45 — The critical trust gap: Teacher student AI views differ
• 03:15 — AI's limitations in social dynamics and emotional understanding
• 04:30 — Student concerns about AI monitoring and judgment
• 05:30 — Data sharing and pedagogical benefits: Student vs. Teacher views
• 06:45 — Autonomous decision making and the need for human guidance
• 08:00 — Addressing the gaps: Metacognition and transparent AI in classroom design
• 09:00 — The human element: Capacity for creativity and connection

What are common teacher student AI views in K-12 education?
The study found students generally trust human teachers more than AI, while teachers often fear students will trust AI more than them, creating a significant "trust gap."

How does AI trust in K-12 differ between students and teachers?
Students express skepticism about AI's ability to understand their emotions and social needs, prioritizing human connection, whereas teachers worry about students perceiving AI as more fair or less biased than themselves.

What are the main challenges for AI in classroom implementation according to this research?
Key challenges include bridging the trust gap, ensuring AI understands social and emotional aspects of learning, managing student concerns about AI monitoring and data privacy, and balancing student autonomy with necessary teacher oversight for learning gains.

Featuring: Dan Fitzpatrick, Tomohiro Nagashima, Lisa Siegrist, Niklas Scholz, Shintaro Sato, Martina Vincoli, Man Su, Saarland University, University of St. Gallen.

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The UN General Assembly's Global Dialogue on AI Governance offers a blueprint for how schools can approach AI policy and AI governance education.

In this episode:

• The UN General Assembly's Global Dialogue on AI Governance demonstrates a global effort to define AI ethics for educators and policymakers, gathering 1,500+ submissions.
• A key divergence in the UN AI recommendations shows governments prioritizing 'capacity-building' while other stakeholders prioritize 'safety,' highlighting critical considerations for AI safety in schools.
• Effective AI governance education involves mirroring the UN's stakeholder-inclusive approach by inviting students, parents, and teachers to shape AI in education policy within their own school communities.
• To bridge the AI divide, schools must implement AI thoughtfully to enhance equity and provide personalized support, ensuring accessibility is foundational, not an afterthought.
• Meaningful human oversight is central to AI literacy, requiring students to develop critical thinking skills to evaluate AI, understand its limitations, and exercise judgment.

Chapters:

• 00:00 — Cold open & welcome
• 00:25 — UN Global Dialogue on AI Governance: Scope and Ambition
• 00:55 — AI Governance Education: A Blueprint for School AI Policy
• 01:40 — Diverging Priorities: Capacity vs. AI Safety in Schools
• 02:25 — Bridging the AI Divide: Equity and AI Accessibility
• 02:50 — Practicalities for Schools: Meaningful Human Oversight and AI Literacy
• 03:30 — UNESCO's Call: Protecting Cultural and Linguistic Heritage with AI
• 03:50 — Co-Creating the Future: The UN AI Recommendations for Educators

How can schools develop an AI in education policy effectively?
Schools can mirror the UN's Global Dialogue on AI Governance by establishing their own school-level 'AI Dialogues' with students, parents, teachers, and leaders to collectively shape policy, rather than just adopting new tools.

What are the main priorities for AI ethics for educators and AI safety in schools?
Global consultations for the UN's dialogue highlighted that while governments prioritize 'capacity-building,' other stakeholders prioritize 'safety,' transparency, accountability, and human oversight, all crucial for AI ethics for educators.

How can AI governance education help bridge the digital divide in schools?
AI governance education must focus on using AI to bridge equity gaps by providing personalized support and differentiation for all students, ensuring accessibility is a foundational principle rather than an afterthought, as highlighted by the International Telecommunication Union.

Featuring: Dan Fitzpatrick, UN General Assembly, António Guterres, Global Dialogue on AI Governance, Independent International Scientific Panel on Artificial Intelligence, Yoshua Bengio, Maria Ressa, International Telecommunication Union (ITU), UNESCO.

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As AI automates more tasks, employers like Anthropic now actively seek recruits with excellent emotional intelligence and people skills.

In this episode:

• Anthropic's co-founder states that as AI advances, "excellent emotional intelligence and people skills" are becoming crucial for employment, highlighting the need for an emotional intelligence curriculum in schools.
• Jean Gross argues for a curriculum re-evaluation to integrate strong communication skills and social emotional learning (SEAL curriculum) across all subjects, not just English, to prepare students for an AI-driven workforce.
• The Education Endowment Foundation (EEF) provides clear evidence that teaching social and emotional skills, such as those found in the comprehensive SEAL curriculum, positively impacts student attainment and overall development.
• Designing assessments that value the "Process" and "Performance"—like empathetic listening and collaborative problem-solving—alongside factual "Product" is essential for an AI soft skills-focused curriculum.
• Educators can leverage existing resources like the freely available SEAL curriculum to explicitly teach emotional intelligence, fostering skills like perspective-taking, conflict resolution, and resilience.

Chapters:

• 00:00 — Cold open & welcome
• 00:30 — Anthropic's demand for emotional intelligence in an AI world
• 01:25 — Why our curriculum needs an emotional intelligence re-evaluation
• 02:10 — Integrating communication skills and oracy across subjects
• 03:25 — Assessment challenges and the need for AI-proof tasks
• 04:30 — The missed opportunity for a dedicated emotional intelligence curriculum
• 05:15 — Evidence and resources for teaching social emotional learning (SEAL curriculum)
• 06:20 — The "human-in-the-loop" advantage: outthinking machines with AI soft skills
• 07:05 — Navigating change: Implementing an emotional intelligence curriculum effectively
• 08:00 — Conclusion: The right road for an AI-age curriculum

Why is an emotional intelligence curriculum becoming more important with AI?
As AI automates more tasks, employers like Anthropic are actively seeking recruits with "excellent emotional intelligence and people skills," making these uniquely human attributes critical for future employment.

How can teachers integrate social emotional learning (SEAL curriculum) across all subjects?
Teachers can weave social emotional learning by redesigning lessons to include empathetic role-playing, collaborative problem-solving with reflection on disagreements, and practicing constructive feedback within subject-specific projects.

What evidence supports teaching emotional intelligence and AI soft skills?
The Education Endowment Foundation (EEF) has found clear evidence that teaching social and emotional skills has a positive impact on a range of student outcomes, including academic attainment.

Featuring: Dan Fitzpatrick, Jean Gross, Anthropic, Claude chatbot, ABC News, The Times, Alan Milburn, Education Endowment Foundation, EEF.

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An AI system built software in 14 hours for $251, a task normally taking a human team months. This is AI agents in education.

In this episode:

• An AI system, Opus 4.7, accomplished a complex software development task in 14 hours that typically requires human teams months, showcasing dramatic AI capability gains.
• The way we interact with AI is evolving from co-intelligence chatbots to autonomous AI agents that complete complex tasks with minimal human oversight, shifting the focus to managing AI in schools.
• Ethan Mollick emphasizes that domain expertise is crucial for maximizing the effectiveness of AI agents, as experts achieve better results when assigning AI for complex tasks.
• Educators must adapt their approach to AI literacy, moving beyond basic prompting to teaching students how to critically manage AI agents, thereby preparing them for the future of AI work.
• The exponential growth of AI means schools must implement continuous professional development for teachers to strategically leverage AI for complex tasks and navigate constant technological shifts.

Chapters:

• 00:00 — Cold open & welcome
• 00:30 — Opus 4.7: Staggering AI capability gains
• 01:00 — Measuring AI's ability to do real work
• 01:45 — Near-frontier AI models and their exponential growth
• 02:30 — Implications for AI agents for educators and workflows
• 03:15 — From chatbots to AI agents: A changing interaction model
• 04:30 — Managing AI in schools: The manager's role in the future of AI work
• 05:45 — Rethinking AI literacy and collaborative reasoning
• 06:45 — The impact of exponential AI capability gains on institutions
• 07:30 — Designing professional development for continuous adaptation

What are AI agents and how do they differ from chatbots for educators?
AI agents are long-running, smart, self-correcting AI systems that tackle complex tasks with less human intervention than traditional chatbots, requiring educators to shift from interactive prompting to managing AI workflows.

How can teachers use AI agents for complex tasks in the classroom?
Teachers can assign AI agents to handle time-consuming administrative tasks, content generation, and aspects of differentiation, freeing up human capacity for unique human skills like judgment and relationship-building.

What is the future of AI work for students and how should schools prepare them?
The future of AI work involves managing AI agents rather than just using chatbots, so schools should teach students to critically evaluate AI outputs, identify biases, and apply human judgment to AI-generated insights.

Featuring: Dan Fitzpatrick, Ethan Mollick, Claude Fable, GPT-5.6, METR, UK’s official government AI Security Institute, GDPval, Epoch, Opus 4.7.

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Only 2% of secondary schools in England have a comprehensive AI strategy, despite AI already being embedded in teaching and learning.

In this episode:

• A new report reveals only 2% of secondary schools in England have a comprehensive AI strategy, despite widespread informal adoption for tasks like lesson planning.
• The primary barrier to implementing AI strategy schools England is a lack of staff confidence and skills (63%), not financial cost.
• School leaders who actively engage with and model AI use foster more consistent AI adoption across their institutions and are key to effective AI staff training schools.
• A clear AI policy education should focus on 'purpose over technology,' defining *why* AI is used, not just *how not to*.
• Uneven AI integration and AI staff training schools, particularly between London and other regions, risk deepening educational disparities in England.

Chapters:

• 00:00 — Cold open & welcome
• 00:25 — Only 2% of schools in England have an AI strategy
• 00:55 — Distinguishing AI policy from AI strategy in education
• 01:30 — Current AI uses for lesson planning and efficiency
• 02:00 — Leadership engagement with AI and perceived risks
• 02:40 — Biggest barriers: staff confidence and AI literacy
• 03:15 — Impact of leadership modelling on AI adoption
• 03:55 — Regional disparities in AI use and widening inequality risks
• 04:50 — Five practical steps for an AI strategy schools England
• 06:50 — Seizing opportunity through purposeful AI strategy

How many schools in England have a comprehensive AI strategy?
Only 2% of secondary schools in England have developed a comprehensive AI strategy, according to a report by Accenture and Teach First, despite AI already being embedded in teaching and learning.

What are the biggest challenges for schools implementing AI?
The primary challenge for schools implementing AI is a lack of staff confidence or skills (63%), followed by data privacy concerns (51%) and a limited understanding of AI's educational potential.

How can school leaders encourage AI adoption among staff?
School leaders can encourage AI adoption by directly engaging with AI, demonstrating responsible use, allowing controlled experimentation, and fostering ongoing shared learning among staff.

Featuring: Dan Fitzpatrick, Accenture, Teach First, Matt Prebble, James Toop, England, London.

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The most capable AI model ever released was shut down by the US government then returned, a wild saga with vital lessons for AI policy for schools.

In this episode:

• Anthropic's Claude Fable 5, the "most capable AI model ever released," experienced a rapid launch, government-mandated shutdown, and return, offering a blueprint for future AI model reliability challenges.
• The 22-day saga of Claude Fable 5 underscores that a robust AI policy for schools must include contingency planning to avoid a single point of failure when integrating AI into workflows.
• The US government's swift intervention with emergency export controls on Fable 5 signals that government AI regulation is a dynamic landscape, necessitating adaptable AI in education strategy based on principles, not specific tools.
• Fable 5's comeback, facilitated by enhanced safety classifiers and a HackerOne program, illustrates that transparent, iterative improvement in AI safety is achievable and builds trust.
• Schools should model Anthropic's transparency by communicating openly about AI tool limitations and actively working to improve them, fostering reflective awareness of AI's capabilities and constraints.

Chapters:

• 00:00 — Cold open & welcome
• 00:30 — The wild saga of Claude Fable 5's launch and shutdown
• 01:25 — Understanding Anthropic's Mythos-class models and safeguards
• 02:40 — Staggering early performance and pricing of Fable 5
• 03:20 — Why the US government pulled the plug on Fable 5
• 04:35 — The engineering and diplomacy behind Fable 5's return
• 05:40 — Lesson 1: Contingency in AI policy for schools – avoiding single points of failure
• 07:15 — Lesson 2: Adapting to government AI regulation with principle-based policies
• 08:25 — Lesson 3: The hopeful truth about AI model reliability and transparency
• 09:40 — The enduring task for educators in an age of extraordinary AI capability

What is Anthropic Fable 5 and why was it temporarily shut down?
Anthropic Fable 5 was a highly capable AI model launched by Anthropic that was temporarily suspended by the US government due to national security concerns after researchers bypassed its safeguards.

How does the Fable 5 incident inform AI policy for schools?
The Fable 5 incident teaches schools to build contingency plans for AI tools, assume dynamic government AI regulation, and anchor their AI in education strategy in adaptable principles rather than specific products.

What does the Fable 5 story tell us about AI model reliability and safety?
Fable 5's return demonstrates that AI model reliability can be enhanced through dedicated engineering work on safeguards, transparent collaboration with regulators, and open communication about limitations, proving safety and capability are not mutually exclusive.

Featuring: Dan Fitzpatrick, Anthropic, Claude Fable 5, Project Glasswing, Claude Opus 4.8, Mythos 5, US government, HackerOne, OpenAI GPT-5.5.

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An AI tutor in Sierra Leone reportedly helped students gain a year of schooling in eight weeks, a claim even Google DeepMind cautions us to take with a grain of salt.

In this episode:

• Google DeepMind's AI tutor education trial in Sierra Leone estimated students gained a year's learning in eight weeks, using a re-engineered Gemini model.
• The AI learning tool, Guided Learning, was designed not to give direct answers, fostering productive struggle crucial for learning outcomes.
• Early findings suggest AI in classrooms might initially widen the gap for already proficient students, prompting Google DeepMind to investigate different pedagogies to support struggling learners.
• Teachers in the Sierra Leone trial adapted quickly to AI learning tools, discovering new teaching strategies and increasing one-on-one student interaction after just one day of training.
• Transparent research practices from Google DeepMind, including a public playbook and training materials, aim to allow wider adoption and scrutiny of AI for learning outcomes.

Chapters:

• 00:00 — Cold open & welcome
• 00:45 — Irina Jurenka's shift to social impact at Google DeepMind
• 01:45 — The core tension: AI as assistant vs. AI for learning
• 02:45 — How Guided Learning became a true AI tutor education tool
• 03:45 — Overcoming the AI's tendency to give answers
• 04:45 — The 'take with a grain of salt' claim on learning gains
• 05:45 — Addressing equity and the 'widening gap' concern in AI for learning outcomes
• 06:45 — Google DeepMind's transparency and research integrity
• 07:45 — Transforming teaching practices with AI learning tools
• 08:45 — The future evolution of AI in classrooms

How did Google DeepMind's AI tutor impact learning in Sierra Leone?
The AI tutor education trial in Sierra Leone estimated students gained approximately one year of schooling in just eight weeks, though this figure comes with a scientific caveat from the researchers.

Can teachers trust AI learning tools not to give students answers directly?
The Guided Learning tool, designed by Google DeepMind for AI in classrooms, was specifically engineered not to give direct answers, forcing students into productive struggle, unlike many public AI models.

Do AI learning tools widen the learning gap, or help all students?
Initial findings from the Sierra Leone trial showed that stronger math students benefited most, but Google DeepMind is actively researching different pedagogical approaches for AI to ensure it helps raise the floor for struggling students too.

Featuring: Dan Fitzpatrick, Irina Jurenka, Google DeepMind, Sierra Leone, Guided Learning, Gemini, World Bank, Stanford, Nigeria.

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70% of people can't spot AI deepfakes, leaving students vulnerable to hidden AI in advertising without proper AI literacy.

In this episode:

• A *Guardian* investigation revealed that 70% of people cannot detect *AI deepfakes*, making students vulnerable to hidden *AI in advertising*.
• Brands like *Once* and *Maket* are using undisclosed *AI generated content* and *AI-generated influencers* due to lower costs and fewer risks compared to human talent.
• Current *AI transparency rules* are lagging; the UK's *Advertising Standards Authority* doesn't explicitly prohibit undisclosed AI, although the EU will require labeling.
• Developing *AI influencers ethics* and critical AI literacy is paramount for educators to equip students to navigate a world saturated with AI-generated information.
• Educators must foster critical thinking skills, including basic *AI deepfake detection*, and discussions around *AI transparency rules* in the classroom.

Chapters:

• 00:00 — Cold open & welcome
• 00:30 — The Guardian's investigation into undisclosed AI in advertising
• 01:15 — Case studies: Once, Maket, and Ashle using AI-generated influencers
• 02:15 — Educational implications: Why AI literacy is crucial for students
• 03:00 — Why brands use AI: Cost savings and managing public image
• 03:45 — The blurring lines of authenticity and 'plausible deniability' in AI content
• 04:30 — Regulatory response: Which?, Lisa Barber, and the Advertising Standards Authority
• 05:15 — The urgent need for AI deepfake detection and AI transparency rules in education
• 06:00 — Cultivating human judgment and ethics over technological prowess

How can teachers equip students to identify *AI deepfakes* and *AI generated content*?
Teachers can equip students by fostering AI literacy as a critical thinking skill, teaching them to question AI outputs, biases, and intent, and discussing *AI transparency rules* in the classroom.

What are the ethical concerns surrounding *AI in advertising* and *AI influencers ethics*?
Ethical concerns include misleading consumers with undisclosed *AI generated content*, eroding trust, and the potential for manipulation when 70% of people cannot detect fake videos.

Are there *AI transparency rules* or regulations for brands using *AI-generated influencers*?
Currently, the UK's *Advertising Standards Authority* does not explicitly prohibit undisclosed *AI generated content*, though the EU's new Artificial Intelligence Act will require deepfakes to be labeled.

Featuring: Dan Fitzpatrick, Once, Maket, Ashle, Reality Defenders, Get Real Labs, Which?, Advertising Standards Authority, Lisa Barber.

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Almost 30% of Saudi teachers already correct biased AI outputs, highlighting an urgent need for students to interrogate AI, not just trust it.

In this episode:

• A 2025 Saudi Arabia survey found nearly 30% of teachers are already correcting AI bias in education, highlighting an urgent need for students to interrogate AI.
• The core issue: most AI tools are trained on English-language and Western-dominant datasets, creating linguistic and cultural blind spots in AI-generated knowledge for diverse learners.
• Teachers must evolve into 'epistemic intermediaries,' guiding students in AI critical thinking education by modeling how to assess AI outputs for accuracy and cultural relevance.
• True AI literacy for students involves collaborative reasoning and actively critiquing AI responses, not just passively accepting them.
• Designing assessment tasks around "Product, Process, and Performance" can ensure students engage in cognitive stretch, applying unique context and judgment, which cannot be faked by AI tools.

Chapters:

• 00:00 — Cold open & welcome
• 00:25 — Saudi Arabia's AI bias challenge: 30% of teachers correcting AI
• 00:55 — Cultural and linguistic blind spots in AI tools
• 01:50 — AI critical thinking education: shifting from teacher as authority to AI interrogator
• 02:45 — The teacher's new role: 'epistemic intermediary' assessing AI outputs
• 03:50 — Redefining AI literacy for students: collaborative reasoning and critique
• 04:45 — Saudi Arabia's proactive approach to addressing AI bias in education
• 05:25 — School leaders: prioritizing AI critical thinking education over technology adoption
• 06:10 — Protecting human judgment, imagination, and wisdom in responsible AI in education
• 06:45 — Knowledge transmission to knowledge interrogation: The core shift

How can teachers address AI bias in education in their classrooms?
Teachers can address AI bias by becoming 'epistemic intermediaries,' systematically assessing AI-generated content with students for factual accuracy, linguistic precision, cultural relevance, and contextual appropriateness.

What does AI critical thinking education look like for students?
AI critical thinking education involves teaching students to systematically critique AI responses, compare outputs across languages, identify inconsistencies, and consciously inject missing cultural nuance into AI-generated content.

Why is responsible AI in education crucial for non-English dominant contexts?
Responsible AI in education is crucial because most AI tools are trained on English-language and Western-dominant datasets, leading to inherent linguistic and cultural blind spots that can misrepresent local realities for students in other regions.

Featuring: Dan Fitzpatrick, Basmah AlBuhairan, Reem Taibah, Amani AlOlayani, King Abdulaziz City for Science and Technology, Centre for the Fourth Industrial Revolution Saudi Arabia, Ministry of Education, Saudi Arabia, World Economic Forum.

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94% of students used AI in assessed work by 2026; learn why AI weakens core knowledge vital for genuine oracy.

In this episode:

• A 2026 Higher Education Policy Institute survey revealed 94% of students used AI in assessed work, necessitating a renewed focus on oral communication education.
• Bruce Hood argues that AI, while not directly harming oracy, weakens the core knowledge and synthesis abilities vital for genuine oral communication, impacting graduate employability skills.
• Employers now prioritize verbal communication and soft skills over grades, highlighting the increasing importance of assessing oral communication for future job markets.
• The Three Minute Thesis (3MT) competition, pioneered by the University of Queensland, demonstrates a successful model for developing advanced oral communication skills in students.
• Educators should shift from solely written assignments to incorporate more oral assessments to build essential AI and oracy skills from primary school through higher education.

Chapters:

• 00:00 — Cold open & welcome
• 00:25 — 94% of students using AI in assessed work
• 00:55 — Employer demand for oral communication education
• 01:30 — How AI weakens core knowledge for AI and oracy skills
• 02:25 — The strategic choice for integrating oral communication education
• 03:05 — Designing learning tasks for oral communication beyond written reports
• 03:50 — Assessing oral communication: Product, Process, and Performance
• 04:30 — Embedding oracy skills through professional development and AI Mavericks
• 05:15 — The Three Minute Thesis (3MT) as a model for developing graduate employability skills
• 05:55 — Oracy as the human differentiator in an AI-augmented world

How does AI impact student skills for oral communication education?
AI can weaken the foundational knowledge and deep synthesis required for effective oral communication by performing too much of the cognitive heavy lifting that students previously did themselves.

What graduate employability skills are most valued by employers in the AI age?
Employers increasingly value verbal communication, critical thinking, authenticity, and the ability to establish credibility and respond on the fly, as reported by the National Association of Colleges and Employers and CBI Economics.

How can educators improve assessing oral communication in the AI era?
Educators can shift focus to oral assessments like debates, presentations, conversational exams, and performance-based tasks that require students to apply knowledge dynamically and respond to spontaneous questions.

Featuring: Dan Fitzpatrick, Bruce Hood, Higher Education Policy Institute, National Association of Colleges and Employers, CBI Economics, University of Queensland, Three Minute Thesis, My-Thesis, University of Bristol.

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