Weibing Zheng, Laurah Turner, Jess Kropczynski, Murat Ozer, Matthew Kelleher, Seth Overla and Shane Halse, University of Cincinnati, USA

Large Language Models (LLMs) have tremendous promisesonconversational tasks in various sectors, including medical education. This study aims to integrate LLMs in medical education by user-centered iterative design and develop an educational clinical scenario simulator for clinical reasoning. The initial iteration prototypes a medical students-AI patient conversational app via prompt engineering. Feedback from physicians, student surveys, and focus group interviewsrevealed needs for a more comprehensive simulation mirroring the multi-agential nature of real clinical encounters. The second iteration prototypesan interactive LLM-based educational scenario simulator for clinical reasoning withan AI patient agent, multiple clinical dataacquisition agents, and educational assistant agents.Post-use surveysindicatetopfavouritesin clinical reasoningdevelopment(72.2%),real-time guidance(47.2%) and information gathering (44.4%). Theprogress from an LLM-powered conversational app to multi-agent educational simulator through iterative cycles with physicians and students-inputestablished a roadmapfor integrating LLMs into medical education and advancingAI-powered educational app design and development.

AI, Medical Education, LLM, Clinical Scenario Simulation, Iterative Design