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 Reading Budzyń 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
