AI for Educators Daily with Dan Fitzpatrick

Dan Fitzpatrick, The AI Educator
  • 4.9 (25)
  • Education
  • Updated Daily

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.

  1. 45m ago

    Rethinking edtech evaluation

    Send us Fan Mail Two-thirds of teachers use AI, but only one in five edtech products has evidence of improving outcomes. In this episode: Nearly two-thirds of teachers use AI, but only 20% of AI edtech products have evidence of improving outcomes, underscoring the urgent need for better AI education research.Traditional randomized controlled trials (RCTs) are often too slow and rigid for evaluating rapidly evolving AI tools, necessitating new education research methods.Stacey Alicea and Meghan McCormick propose 'implementation research and development' as a robust framework for assessing AI tool effectiveness through iterative testing and refinement.Three guiding principles for AI edtech evaluation are: building evidence in stages, asking 'how it works' before 'whether it works,' and letting specific research questions dictate the methodology.The Research Partnership for Professional Learning's Shared Measures Toolkit demonstrates effective, iterative evaluation, building measurement infrastructure crucial for responsible AI in classrooms.Chapters: 00:00 — Cold open & welcome00:45 — The problem: AI use outpaces AI edtech evaluation01:30 — Why traditional RCTs fail for AI education research02:45 — Introducing implementation research and development (R&D) for AI tool effectiveness03:45 — National efforts embracing iterative AI edtech evaluation04:30 — Principle 1: Build AI evidence in stages (feasibility first)05:30 — Principle 2: Ask 'how it works' before 'whether it works' for AI in classrooms06:45 — Principle 3: Let research questions drive the education research methods08:00 — Implications for school leaders and the need for faster evidence09:00 — Example: Research Partnership for Professional Learning's Shared Measures ToolkitHow can we evaluate new AI tools in education more effectively? To evaluate new AI tools effectively, educators should shift from relying solely on slow randomized controlled trials to iterative 'implementation research and development' that rapidly tests and refines tools in real-world settings. Why are traditional education research methods not working for AI? Traditional education research methods like randomized controlled trials are often too slow and designed for static interventions, making them unsuitable for the rapid and continuous evolution of AI tools in education. What is implementation research and development for AI in education? Implementation research and development (R&D) is an approach that prioritizes rapid testing, feedback, and refinement of early-stage AI products to understand their design, delivery, and real-world usage, providing initial evidence on their effects before large-scale trials. Featuring: Dan Fitzpatrick, Stacey Alicea, Meghan McCormick, Institute of Education Sciences, Leanlab Education, Boston University's EVAL initiative, Teaching Lab, Research Partnership for Professional Learning, Shared Measures Toolkit. Follow AI in Education with Dan Fitzpatrick for more on AI in education.

    Rethinking edtech evaluation

  2. 1d ago

    AI detection & academic integrity

    Send us Fan Mail A student wrongly accused by Turnitin needed a court ruling to clear his name, highlighting profound AI detection false positives. In this episode: A New York court cleared a student wrongly accused of AI use by Turnitin, highlighting the critical issue of AI detection false positives.More than 40% of UK universities lack publicly accessible AI policy, contributing to student anxiety and reluctance to use AI for learning.Experts advocate for comprehensive AI assessment design, urging educators to focus on tasks requiring unique human judgment and critical thinking rather than relying on unreliable AI detection tools for academic integrity AI.The American Association of Colleges and Universities cautions that AI detection tools should only play a minor role in academic integrity cases due to high false positive rates and potential bias, especially against non-native English speakers.Universities must provide clear, consistent guidance on AI use and transparent processes to build trust and ensure fairness in an era of rapid technological change, as unreliable AI detection is not the answer.Chapters: 00:00 — Cold open & welcome00:27 — Orion Newby's case: A shocking example of AI detection false positives01:21 — The scale of AI use and the rise of detection tools02:08 — Why AI detection tools are failing educators and the primary concern of false positives03:10 — Leading universities restrict AI detection due to ethical concerns and bias03:57 — Rethinking AI assessment design for true academic integrity04:51 — The 'Three Ps' of assessment: Product, Process, and Performance05:43 — The urgent need for clear AI policy in universities06:40 — Building trust and consistency in AI use across institutions07:33 — Empowering students: Beyond surveillance to authentic human thinkingHow reliable are AI detection tools like Turnitin, GPTZero, and Copyleaks in identifying AI-generated content? AI detection tools are currently unreliable and prone to significant AI detection false positives, meaning they can falsely accuse students of using AI when they haven't. What are the risks of using AI detection tools for academic integrity in universities? The primary risks include false accusations, disproportionate impact on non-native English speakers, student anxiety, and undermining trust in the academic process, as reliable AI detection is not yet possible. What is an effective approach for universities to maintain academic integrity in the age of AI? An effective approach involves redesigning assessments to require unique human thinking and critical analysis, fostering transparency in AI policy universities, and moving away from over-reliance on unreliable AI detection tools. Featuring: Dan Fitzpatrick, Turnitin, GPTZero, Copyleaks, OpenAI, ChatGPT, Edinburgh Napier University, Queen's University Belfast, American Association of Colleges and Universities. Follow AI in Education with Dan Fitzpatrick for more on AI in education.

    AI detection & academic integrity

  3. Jul 17

    Are these future-proof careers?

    Send us Fan Mail Only 47% of parents would recommend hands-on careers to their kids, despite experts saying these are the most AI proof career paths. In this episode: Only 47% of parents recommend hands-on careers, despite experts identifying them as AI resistant jobs due to their reliance on irreplaceable human connection and nuanced judgment.The core of an AI proof teaching career lies in human elements like empathy, bespoke care, and relationship-building, which AI cannot replicate, making teaching a fundamentally human endeavor.AI impact education will be seen as roles evolve rather than disappear; educators can leverage AI for administrative tasks, freeing them to focus on complex student needs and fostering higher-order thinking.AI literacy, including understanding AI limitations and developing collaborative reasoning, is becoming a critical skill for students and AI for educators.Practical steps for leaders involve connecting AI to existing teacher friction points, fostering teacher wellbeing, and empowering them as change agents to drive innovation, not just tool adoption.Chapters: 00:00 — Cold open & welcome00:54 — What makes jobs AI resistant and irreplaceable?02:15 — Childcare and teaching: Why human connection is critical for an AI proof teaching career03:45 — How roles will evolve, not disappear, reflecting AI impact education05:15 — AI in hospitality: Focusing on human connection and capacity for creativity06:15 — Cultivating AI literacy: Collaborative reasoning for AI for educators07:00 — AI as an equalizer: Enhancing accessibility in education and childcare07:45 — Redesigning assessment for the AI era: Demanding depth and human judgment08:45 — Practical steps for leaders: Anchoring AI to teacher needs and measuring wellbeing09:45 — The future of work: Leaning into uniquely human skillsWhat makes a teaching career AI proof? A teaching career is AI proof because it relies on irreplaceable human connection, nuanced judgment, empathetic care, and the ability to inspire, which machines cannot replicate. How will AI impact education for teachers? AI will impact education by automating administrative and repetitive tasks, allowing teachers to focus more on complex student needs, foster relationships, and develop higher-order thinking skills through human oversight and judgment. What skills should educators and students develop to thrive in an AI-powered world? Educators and students should develop AI literacy, collaborative reasoning, critical thinking about AI limitations, and the uniquely human skills of wonder, care, judgment, relationship building, and imagination. Featuring: Dan Fitzpatrick, Guardian Design, Oushk Pharmacy, Oxford University’s Generational Success Lab, Tiney, Lawhive, Law Society of England and Wales, Westmont Institute of Tourism and Hospitality at Nova School of Business and Economics. Read the original source Follow AI in Education with Dan Fitzpatrick for more on AI in education.

    Are these future-proof careers?

  4. Jul 16

    What employers now demand from new hires

    Send us Fan Mail 100% of one bank department uses generative AI daily, proving AI isn't replacing expertise, but drastically raising the bar for it. In this episode: A Harvard Business Review study by Jim Doucette and Vishal Gaur found that 100% of a bank department now uses generative AI daily, highlighting rapid AI hiring changes.The AI impact on jobs is not about replacing expertise but significantly raising the bar for it, demanding enhanced human judgment and critical thinking.Educators can prepare students for AI workplace skills by integrating AI tools for initial drafts and then requiring critical evaluation and transformation using frameworks like EDIT.Curriculum design must evolve to assess higher-order thinking, ensuring tasks require unique human context, perspective, or judgment beyond what AI can produce.Cultivating 'collaborative reasoning ability'—understanding AI limitations and precision in prompts—is crucial for future generative AI employment.Chapters: 00:00 — Cold open & welcome00:30 — Harvard Business Review research on AI hiring changes01:00 — AI raises the bar for expertise, not replaces it01:45 — Preparing students to operate 'above' AI tools02:15 — The EDIT framework for developing AI workplace skills02:45 — Redesigning assessment for the AI impact on jobs03:15 — Curriculum review for school leaders and department heads03:45 — Collaborative reasoning and generative AI employment04:15 — The enduring value of human judgment and creativityHow is AI changing what employers want from new hires? Employers now seek candidates who can critically analyze and strategically transform AI outputs, rather than just performing routine tasks, effectively raising the bar for expertise. What AI workplace skills should educators focus on teaching? Educators should focus on teaching students to evaluate, determine accuracy, identify bias, and transform AI-generated content, moving beyond mere tool usage to higher-order thinking and judgment. How can schools adapt curriculum to address AI hiring changes? Schools need to redesign tasks to demand unique human context, perspective, and judgment, ensuring assessments cannot be fully completed by AI and foster collaborative reasoning abilities for generative AI employment. Featuring: Dan Fitzpatrick, Harvard Business Review, Jim Doucette, Vishal Gaur. Read the original source Follow AI in Education with Dan Fitzpatrick for more on AI in education.

    What employers now demand from new hires

  5. Jul 15

    AI, good intentions & falling math scores

    Send us Fan Mail A 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.

    AI, good intentions & falling math scores

  6. Jul 14

    It's all about trust for both students and teachers

    Send us Fan Mail 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 & 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.

    It's all about trust for both students and teachers

  7. Jul 13

    UN global dialogue for AI in schools

    Send us Fan Mail 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 & 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 source Follow AI in Education with Dan Fitzpatrick for more on AI in education.

    UN global dialogue for AI in schools

  8. Jul 10

    Emotional Intelligence: The Essential Skill

    Send us Fan Mail 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 & 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 source Follow AI in Education with Dan Fitzpatrick for more on AI in education.

    Emotional Intelligence: The Essential Skill
See All (333)

4.9
out of 5
25 Ratings

About

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.

Information

You Might Also Like