Podcast
AI for Educators Daily with Dan Fitzpatrick
Hosted by Dan Fitzpatrick, The AI Educator · EN
Hey, I'm Dan, The AI Educator.
I know that we both care deeply about the state of education, amid the uncertainty of rapidly advancing AI. I work with leading schools and governments worldwide to help them strategise and build capability, and I have recently been recognised as a top voice on AI. While most teachers are aware of the influence of AI on education and student learning, many are unsure how to respond in practice.
My mission is to amplify credible expert insight and give educators the clarity, confidence, and tools they need to teach effectively and prepare students.
115episodes
Episodes
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AI, good intentions & falling math scores
Yesterday00:10:35Summary readySend us Fan MailA new study reveals a statistically significant drop in adolescent math scores after using an AI tutor for exam prep, despite students' good intentions.In this episode:A study from the University of Tübingen revealed a significant 10-point drop in adolescent math scores after using an AI tutor for exam prep, indicating challenges in self-regulated learning with AI.The research identified a large gap between students' good intentions for learning and their actual, often superficial, help-seeking GenAI interactions, with monitoring and evaluation being nearly absent.Higher extraneous cognitive load, caused by the demands of navigating AI tutor adolescent learning, predicted lower math scores, highlighting how AI can inadvertently hinder deep learning.Effective AI math education requires explicitly teaching students metacognitive skills like epistemic vigilance and agency over the AI, not just providing access to the technology.Educators should design tasks that embed the process of AI interaction, such as annotating chat logs, to foster crucial self-regulated learning AI behaviors.Chapters:00:00 — Cold open & welcome00:25 — AI tutor adolescent learning: The shocking math score drop00:55 — Understanding self-regulated learning AI challenges01:30 — Intentions vs. enactment: The gap in student AI use02:30 — The impact of extraneous cognitive load on AI math education03:40 — Explicitly teaching help-seeking GenAI strategies04:30 — Cultivating epistemic vigilance and agency over the AI05:25 — School leader implications: Purpose over technology06:05 — Designing for thinking and reflective AI engagementHow does using an AI tutor affect adolescent math scores?A study found that adolescent students experienced a statistically significant drop in their math performance after using an AI tutor for exam preparation, despite having good intentions for learning.What is self-regulated learning AI and why is it important?Self-regulated learning AI refers to students' ability to monitor and evaluate their own comprehension and the AI's responses, which is crucial for preventing passive learning and ensuring the AI truly supports deeper engagement.How can teachers minimize AI cognitive load in math education?Teachers can minimize AI cognitive load by explicitly teaching students how to formulate effective prompts, manage AI conversations, and design tasks that scaffold metacognitive skills like monitoring and evaluating AI outputs, rather than simply giving access to the tool.Featuring: Dan Fitzpatrick, Rania Abdelghani, Peter Kaiser, Kou Murayama, University of Tübingen, Mistral-Large, Zimmerman's cyclical model, Gemini 2.5 Pro, Baden-Württemberg.Follow AI in Education with Dan Fitzpatrick for more on AI in education.
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It's all about trust for both students and teachers
2d ago00:15:29Summary readySend us Fan MailStudents 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 & welcome00:30 — Mind the Trust Gap: Research overview with Tomohiro Nagashima and team01:15 — How Intelligent Tutoring Systems (ITS) were explored01:45 — The critical trust gap: Teacher student AI views differ03:15 — AI's limitations in social dynamics and emotional understanding04:30 — Student concerns about AI monitoring and judgment05:30 — Data sharing and pedagogical benefits: Student vs. Teacher views06:45 — Autonomous decision making and the need for human guidance08:00 — Addressing the gaps: Metacognition and transparent AI in classroom design09:00 — The human element: Capacity for creativity and connectionWhat 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.Follow AI in Education with Dan Fitzpatrick for more on AI in education.
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UN global dialogue for AI in schools
3d ago00:08:20Summary readySend us Fan MailThe 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 & welcome00:25 — UN Global Dialogue on AI Governance: Scope and Ambition00:55 — AI Governance Education: A Blueprint for School AI Policy01:40 — Diverging Priorities: Capacity vs. AI Safety in Schools02:25 — Bridging the AI Divide: Equity and AI Accessibility02:50 — Practicalities for Schools: Meaningful Human Oversight and AI Literacy03:30 — UNESCO's Call: Protecting Cultural and Linguistic Heritage with AI03:50 — Co-Creating the Future: The UN AI Recommendations for EducatorsHow 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.Read the original sourceFollow AI in Education with Dan Fitzpatrick for more on AI in education.
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Emotional Intelligence: The Essential Skill
6d ago00:10:57Summary readySend us Fan MailAs 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 & welcome00:30 — Anthropic's demand for emotional intelligence in an AI world01:25 — Why our curriculum needs an emotional intelligence re-evaluation02:10 — Integrating communication skills and oracy across subjects03:25 — Assessment challenges and the need for AI-proof tasks04:30 — The missed opportunity for a dedicated emotional intelligence curriculum05:15 — Evidence and resources for teaching social emotional learning (SEAL curriculum)06:20 — The "human-in-the-loop" advantage: outthinking machines with AI soft skills07:05 — Navigating change: Implementing an emotional intelligence curriculum effectively08:00 — Conclusion: The right road for an AI-age curriculumWhy 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.Read the original sourceFollow AI in Education with Dan Fitzpatrick for more on AI in education.
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Maximizing capability gains for AI agents
1w ago00:11:16Summary readySend us Fan MailAn 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 & welcome00:30 — Opus 4.7: Staggering AI capability gains01:00 — Measuring AI's ability to do real work01:45 — Near-frontier AI models and their exponential growth02:30 — Implications for AI agents for educators and workflows03:15 — From chatbots to AI agents: A changing interaction model04:30 — Managing AI in schools: The manager's role in the future of AI work05:45 — Rethinking AI literacy and collaborative reasoning06:45 — The impact of exponential AI capability gains on institutions07:30 — Designing professional development for continuous adaptationWhat 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.Read the original sourceFollow AI in Education with Dan Fitzpatrick for more on AI in education.
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Only 2% have an AI strategy
1w ago00:10:29Summary readySend us Fan MailOnly 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 & welcome00:25 — Only 2% of schools in England have an AI strategy00:55 — Distinguishing AI policy from AI strategy in education01:30 — Current AI uses for lesson planning and efficiency02:00 — Leadership engagement with AI and perceived risks02:40 — Biggest barriers: staff confidence and AI literacy03:15 — Impact of leadership modelling on AI adoption03:55 — Regional disparities in AI use and widening inequality risks04:50 — Five practical steps for an AI strategy schools England06:50 — Seizing opportunity through purposeful AI strategyHow 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.Follow AI in Education with Dan Fitzpatrick for more on AI in education.
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Fable 5's return
1w ago00:14:18Summary readySend us Fan MailThe 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 & welcome00:30 — The wild saga of Claude Fable 5's launch and shutdown01:25 — Understanding Anthropic's Mythos-class models and safeguards02:40 — Staggering early performance and pricing of Fable 503:20 — Why the US government pulled the plug on Fable 504:35 — The engineering and diplomacy behind Fable 5's return05:40 — Lesson 1: Contingency in AI policy for schools – avoiding single points of failure07:15 — Lesson 2: Adapting to government AI regulation with principle-based policies08:25 — Lesson 3: The hopeful truth about AI model reliability and transparency09:40 — The enduring task for educators in an age of extraordinary AI capabilityWhat 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.Follow AI in Education with Dan Fitzpatrick for more on AI in education.
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DeepMind’s surprising Sierra Leone trial
1w ago00:14:13Summary readySend us Fan MailAn 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 & welcome00:45 — Irina Jurenka's shift to social impact at Google DeepMind01:45 — The core tension: AI as assistant vs. AI for learning02:45 — How Guided Learning became a true AI tutor education tool03:45 — Overcoming the AI's tendency to give answers04:45 — The 'take with a grain of salt' claim on learning gains05:45 — Addressing equity and the 'widening gap' concern in AI for learning outcomes06:45 — Google DeepMind's transparency and research integrity07:45 — Transforming teaching practices with AI learning tools08:45 — The future evolution of AI in classroomsHow 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.Follow AI in Education with Dan Fitzpatrick for more on AI in education.
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Navigating AI-generated content
2w ago00:07:50Summary readySend us Fan Mail70% 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 & welcome00:30 — The Guardian's investigation into undisclosed AI in advertising01:15 — Case studies: Once, Maket, and Ashle using AI-generated influencers02:15 — Educational implications: Why AI literacy is crucial for students03:00 — Why brands use AI: Cost savings and managing public image03:45 — The blurring lines of authenticity and 'plausible deniability' in AI content04:30 — Regulatory response: Which?, Lisa Barber, and the Advertising Standards Authority05:15 — The urgent need for AI deepfake detection and AI transparency rules in education06:00 — Cultivating human judgment and ethics over technological prowessHow 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.Follow AI in Education with Dan Fitzpatrick for more on AI in education.
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AI Critical Thinking Education: Addressing Bias in Classroom AI
2w ago00:08:47Summary readySend us Fan MailAlmost 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 & welcome00:25 — Saudi Arabia's AI bias challenge: 30% of teachers correcting AI00:55 — Cultural and linguistic blind spots in AI tools01:50 — AI critical thinking education: shifting from teacher as authority to AI interrogator02:45 — The teacher's new role: 'epistemic intermediary' assessing AI outputs03:50 — Redefining AI literacy for students: collaborative reasoning and critique04:45 — Saudi Arabia's proactive approach to addressing AI bias in education05:25 — School leaders: prioritizing AI critical thinking education over technology adoption06:10 — Protecting human judgment, imagination, and wisdom in responsible AI in education06:45 — Knowledge transmission to knowledge interrogation: The core shiftHow 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.Follow AI in Education with Dan Fitzpatrick for more on AI in education.
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