Customizing LLM/AI for student learning in health professions education
Presentation on 12 November 2026 at 1300hrs
As part of Course - "From Data to Diagnosis: Teaching Clinical Reasoning with AI and EMRs"
DEPARTMENT OF MEDICAL EDUCATION AND BIOETHICS
and
DEPARTMENT OF HEALTH POLICY AND MANAGEMENT
FACULTY OF MEDICINE, PUBLIC HEALTH, AND NURSING
UNIVERSITAS GADJAH MADA, INDONESIA
Customized LLMs and Generative AI transform student learning in health professions education by shifting passive instruction into interactive, personalized coaching.
Thesen, T., & Park, S. H. (2025). A generative AI teaching assistant for personalized learning in medical education. NPJ digital medicine, 8(1), 627. https://doi.org/10.1038/s41746-025-02022-1
Zhui, L., Yhap, N., Liping, L., Zhengjie, W., Zhonghao, X., Xiaoshu, Y., Hong, C., Xuexiu, L., & Wei, R. (2024). Impact of Large Language Models on Medical Education and Teaching Adaptations. JMIR medical informatics, 12, e55933. https://doi.org/10.2196/55933
(following with assistance of Google AI)
Key Customization Strategies for Learning
• Retrieval-Augmented Generation (RAG): Restricts LLM responses strictly to instructor-curated textbooks, clinical guidelines, and lecture notes to eliminate hallucinations and maintain pedagogical accuracy.
Thesen, Thomas & Park, Soo Hwan. (2025). A generative AI teaching assistant for personalized learning in medical education. npj Digital Medicine. 8. 10.1038/s41746-025-02022-1.
• Virtual Patient Simulations: Configures LLMs to role-play diverse clinical cases, allowing students to practice history-taking, differential diagnosis, and bedside communication with immediate automated feedback.
Lai, N. M., Lim, Y. S., Win, M. T., Bhargava, P., Thomas, P., & Ong, Q. C. (2026). The Effectiveness of Artificial Intelligence in Undergraduate Health Professions Education: Systematic Review and Meta-Analysis of Randomized Controlled Trials. JMIR medical education, 12, e88933. https://doi.org/10.2196/88933
• Adaptive Study Plans & Content Generation: Automatically converts dense source material into tailored flashcards (e.g., Anki-ready formats), practice multiple-choice questions (MCQs), short-answer questions (SAQs), and customized outlines.
Zhang, Q., Huang, Z., Huang, Y., Wang, G., Zhang, R., Yang, J., Cheng, Y., Chen, B., Wang, H., Qiu, K., & Chen, H. (2025). Generative AI in medical education: feasibility and educational value of LLM-generated clinical cases with MCQs. BMC medical education, 25(1), 1502. https://doi.org/10.1186/s12909-025-08085-8
Sridharan, K., & Sivaramakrishnan, G. (2025). Large language models as educational collaborators: developing non-conventional teaching aids in pharmacology & therapeutics. BMC medical education, 25(1), 1525. https://doi.org/10.1186/s12909-025-08134-2
Lightning Demos: AI Tools for Medical Education | AI in Medical Education Symposium
• Passive Audio Tools: Transforms written medical summaries into conversational AI podcasts for review during commutes or clinical rotations.
Lightning Demos: AI Tools for Medical Education | AI in Medical Education Symposium
Benefits & Opportunities
• Scalable Personalization: Mitigates the scalability crisis in medical and nursing training by providing 24/7 one-on-one "tutoring" support when human faculty are unavailable.
• Targeted Remediation: Dynamically adjusts difficulty and focuses on individual student knowledge gaps identified during self-assessment.
• High-Stakes Prep: Intensifies active practice during exam periods and clerkship transitions.
Khakpaki A. (2025). Advancements in artificial intelligence transforming medical education: a comprehensive overview. Medical education online, 30(1), 2542807. https://doi.org/10.1080/10872981.2025.2542807
Thesen, Thomas & Park, Soo Hwan. (2025). A generative AI teaching assistant for personalized learning in medical education. npj Digital Medicine. 8. 10.1038/s41746-025-02022-1.
Thesen, T., & Park, S. H. (2025). A generative AI teaching assistant for personalized learning in medical education. NPJ digital medicine, 8(1), 627. https://doi.org/10.1038/s41746-025-02022-1
Challenges & Guardrails
• Accuracy & Verification: Over 70% of health professions students actively cross-check LLM outputs against primary literature or source material to guard against medical inaccuracies or "hallucinations".
• Overreliance & Critical Thinking: Risk of cognitive offloading where students passively accept AI-generated clinical reasoning rather than building foundational diagnostic skills.
Abd-Alrazaq, A., AlSaad, R., Alhuwail, D., Ahmed, A., Healy, P. M., Latifi, S., Aziz, S., Damseh, R., Alabed Alrazak, S., & Sheikh, J. (2023). Large Language Models in Medical Education: Opportunities, Challenges, and Future Directions. JMIR medical education, 9, e48291. https://doi.org/10.2196/48291
• Equity, Privacy, and Ethics: Concerns regarding data privacy, institutional governance, algorithmic bias, and academic integrity policies.
Abd-Alrazaq, A., AlSaad, R., Alhuwail, D., Ahmed, A., Healy, P. M., Latifi, S., Aziz, S., Damseh, R., Alabed Alrazak, S., & Sheikh, J. (2023). Large Language Models in Medical Education: Opportunities, Challenges, and Future Directions. JMIR medical education, 9, e48291. https://doi.org/10.2196/48291
Zhui, L., Yhap, N., Liping, L., Zhengjie, W., Zhonghao, X., Xiaoshu, Y., Hong, C., Xuexiu, L., & Wei, R. (2024). Impact of Large Language Models on Medical Education and Teaching Adaptations. JMIR medical informatics, 12, e55933. https://doi.org/10.2196/55933
