MedSimAI: Simulation and Formative Feedback Generation to Enhance Deliberate Practice in Medical Education
Journal:
arXiv
Published Date:
Mar 1, 2025
Abstract
Medical education faces challenges in scalability, accessibility, and
consistency, particularly in clinical skills training for physician-patient
communication. Traditional simulation-based learning, while effective, is
resource-intensive, difficult to schedule, and often highly variable in
feedback quality. Through a collaboration between AI, learning science, and
medical education experts, we co-developed MedSimAI, an AI-powered simulation
platform that enables deliberate practice, self-regulated learning (SRL), and
automated assessment through interactive patient encounters. Leveraging large
language models (LLMs), MedSimAI generates realistic clinical interactions and
provides immediate, structured feedback using established medical evaluation
frameworks such as the Master Interview Rating Scale (MIRS). In a pilot study
with 104 first-year medical students, we examined engagement, conversation
patterns, and user perceptions. Students found MedSimAI beneficial for
repeated, realistic patient-history practice. Conversation analysis revealed
that certain higher-order skills were often overlooked, though students
generally performed systematic histories and empathic listening. By integrating
unlimited practice opportunities, real-time AI assessment, and SRL principles,
MedSimAI addresses key limitations of traditional simulation-based training,
making high-quality clinical education more accessible and scalable.
