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The Precision Educator

The Precision Educator

Written by: Stanford Anesthesia Informatics and Media (AIM) Lab
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The Precision Educator Podcast examines how coaching, assessment, learning analytics, and AI are shaping precision education in medicine. Hosted by leaders from Stanford Medicine, Yale Medicine, and the Society for Education in Anesthesia, the podcast is designed for program directors, clinician-educators, and education researchers seeking practical, evidence-informed approaches to improve learning and patient care.

© 2026 The Precision Educator
Episodes
  • Gen AI: Inside the First Generation of AI-Native Medical Students

    Gen AI: Inside the First Generation of AI-Native Medical Students
    Aug 10 2026
    For the first time in medical education, a generation of students is learning medicine alongside generative AI. They are using AI to explain physiology, prepare for clinical encounters, explore unfamiliar diagnoses, find current evidence, support research, and ask questions they may hesitate to ask a faculty member. What does medical education look like from the perspective of learners already living in this new reality? In this episode of The Precision Educator, Dr. Larry Chu and Dr. Viji Kurup turn the conversation over to three medical students with distinctly different experiences using AI. Morgan Brinker, Shad Skawratananond, and Chance Lee describe how tools such as ChatGPT and OpenEvidence have become integrated into their day-to-day learning, from filling conceptual gaps left by flashcard-based studying to preparing for cases and rapidly exploring new clinical questions. Their experiences offer a window into what it actually means to learn medicine when an always-available AI tutor is never more than a screen away. The students also reveal an important tension. AI offers immediacy, personalization, and psychological safety, particularly for questions learners feel they “should already know.” At the same time, they remain wary of hallucinations, overreliance, loss of independent information-seeking skills, and the possibility that AI-assisted performance can be mistaken for genuine understanding. They describe deliberately cross-checking sources and identify clinical judgment, experience, mentorship, and human relationships as areas they are unwilling to hand over to AI. The conversation ultimately returns to precision education itself. Students want learning that adapts to what they already know, identifies where they need help, and gives them the right learning opportunity at the right time. AI may finally make that degree of personalization possible at scale. Yet when asked to imagine an AI capable of replacing an attending, the students push back. What they value in great educators extends beyond knowledge transfer to coaching, judgment, empathy, belonging, professional identity, and relationships. For educators, their message is clear: learners are already changing how they learn. The question is no longer whether AI will enter medical education, but how educators can help learners use it thoughtfully while protecting the deeply human dimensions of becoming a physician. Key Takeaways From This EpisodeAI is already part of how medical students learn. Learners are using it as an always-available tutor, research assistant, and clinical learning partner. Personalization may be AI’s greatest educational opportunity. Students want learning that adapts to what they know, what they need, and where they are in their development. Psychological safety matters. AI gives learners a place to ask basic questions without fear of embarrassment or evaluation. AI assistance is not the same as mastery. Hallucination, overreliance, and cognitive deskilling remain important risks, making verification and independent reasoning essential. Students still want human educators. Clinical judgment, mentorship, coaching, empathy, belonging, and professional identity remain fundamentally relational aspects of learning. Related EpisodesWho Teaches the Machine? AI and the Future of Precision Education A complementary exploration of AI from the educator and system perspective, examining how we shape AI-enabled education as these technologies become increasingly capable.When Data Becomes a Coach: Rethinking Assessment, Coaching, and Learning Trajectories Explores how learner data can move beyond assessment toward individualized feedback, coaching, and longitudinal development, directly connecting to the students’ desire for adaptive learning.Talk Less, Listen More: Coaching as Precision Education Examines the distinctly human side of precision education: coaching, relationships, and helping learners develop beyond knowledge acquisition. It pairs particularly well with the students’ insistence that even highly capable AI cannot replace meaningful mentorship.Send us Fan Mail
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    43 mins
  • Who Teaches the Machine? AI and the Future of Precision Education

    Who Teaches the Machine? AI and the Future of Precision Education
    Jun 2 2026
    Medicine has embraced personalization. Every day, clinicians tailor treatments, medications, and interventions to the needs of individual patients. Yet medical education often remains rooted in standardized pathways, where learners progress through the same curriculum despite arriving with different experiences, strengths, and learning needs.In this episode of The Precision Educator, Dr. Larry Chu and Dr. Viji Kurup are joined by Dr. Rishi Kadakia of UCSF to explore how artificial intelligence may help bridge this gap and advance the vision of precision education.The conversation examines the limitations of one-size-fits-all training and the challenge of understanding what learners truly need in order to grow. Dr. Kadakia shares his work using AI-enabled educational tools to support learner preparation, identify knowledge gaps, and create more individualized learning experiences. Together, the guests explore how data generated through learner interactions may provide educators with new ways to understand learner development and tailor educational support.A central theme is AI literacy. As large language models become increasingly accessible to both faculty and trainees, how can educators ensure these tools are used thoughtfully and responsibly? The discussion explores the opportunities AI creates for learning, as well as the risks of overreliance, false confidence, and the gradual erosion of critical thinking and clinical reasoning skills.The episode also highlights the importance of maintaining a “human in the loop” approach. While AI can help organize information, identify patterns, and expand access to educational resources, it cannot replace the contextual judgment, mentorship, and human understanding that define effective teaching. From subtle shifts in patient behavior to the complexities of clinical decision-making, many of the most important educational and clinical signals remain deeply human.Ultimately, this conversation reframes AI not as a destination, but as a tool. The future of precision education will depend not only on technological innovation, but on educators who are willing to understand these tools, shape their use, and ensure they serve the goals of learning, professional development, and patient care.Key takeaways from this episode:Why personalized learning remains one of the greatest challenges in medical educationHow AI may help educators better understand learner needs and support individualized developmentThe growing importance of AI literacy for faculty, trainees, and educational leadersWhy maintaining a “human in the loop” is essential for safe and effective AI implementationHow educators can help shape the future of AI rather than simply react to itEspecially useful for:Medical educators, residency and fellowship leaders, clinician-educators, faculty developers, medical students, residents, and anyone interested in the intersection of artificial intelligence, learning, assessment, and precision education.Related episodes:For an introduction to the foundational principles behind this series, start with Episode 1: What Is Precision Education? Rethinking How Physicians Learn.For a deeper exploration of coaching as a core mechanism of precision education, listen to Episode 2: Talk Less, Listen More: Coaching as Precision Education.For a systems-level perspective on how data can inform learning trajectories and assessment, explore Episode 3: When Data Becomes a Coach: Rethinking Assessment, Coaching, and Learning Trajectories.For insights into how educational leaders balance data, trust, and learner development, listen to Episode 4: From Systems to Signals: Precision Education for Program Directors.Additional ReadingBudzyń K, Romańczyk M, Kitala D et al. Endoscopist deskilling risk after exposure to artificial intelligence in colonoscopy: a multicentre, observational study The Lancet Gastroenterology & Hepatology, 2025; 10, 896-903. Link.Teach (Teaching Educators AI Competency Hands-On), Link.Send us Fan Mail
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    42 mins
  • From Systems to Signals: Precision Education for Program Directors

    From Systems to Signals: Precision Education for Program Directors
    Feb 26 2026

    Residency programs are built on structure—rotations, case minimums, milestones, accreditation requirements. Yet within that framework are individual learners progressing at different speeds, with different strengths and needs. For program directors, the challenge is constant: how do you balance systems and people, standardization and flexibility, accountability and growth?

    In this episode of The Precision Educator, Dr. Larry Chu and Dr. Viji Kurup are joined by Dr. Bryan Mahoney of Mount Sinai and Dr. Marianne Chen of Stanford to explore precision education through the program director lens.

    The conversation examines the daily realities of program leadership: production pressures, limited time, fragmented feedback systems, uneven faculty engagement, and the difficulty of identifying struggling learners early. Both guests reflect on the limits of one-size-fits-all training and the practical strategies they use—from leveraging trusted relationships to developing longitudinal data dashboards—to create more responsive, individualized support.

    A central theme is trust. As programs collect more learner data, how can feedback remain formative rather than punitive? How do leaders protect psychological safety while still acting on meaningful signals? The episode explores the ethical guardrails required to use educational data responsibly and the indispensable role of human mentorship in interpreting it.

    Ultimately, this discussion reframes precision education as leadership work—less about technology, and more about culture, relationships, and the thoughtful integration of data with human judgment to promote not only competence, but human flourishing.

    Key takeaways from this episode:

    • Why program directors operate at the intersection of systems and individual learner growth
    • How fragmented feedback, time constraints, and production pressures limit individualized education
    • The central role of trust, relationships, and psychological safety in using learner data
    • Practical ways to make precision education participatory, actionable, and feasible within real-world constraints

    Especially useful for:

    Program directors, associate program directors, clinician-educators, CCC members, GME leaders, and faculty mentors interested in integrating data-informed approaches into residency training while preserving trust, humanity, and professional growth.

    Related episodes:

    For an introduction to the foundational principles behind this series, start with Episode 1: What Is Precision Education? Rethinking How Physicians Learn.

    For a deeper exploration of coaching as a core mechanism of precision education, listen to Episode 2: Talk Less, Listen More: Coaching as Precision Education.

    For a systems-level perspective on how data can inform learning trajectories and assessment, explore Episode 3: When Data Becomes a Coach: Rethinking Assessment, Coaching, and Learning Trajectories.

    Send us Fan Mail
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    48 mins
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