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Simulation-based learning has become a cornerstone of healthcare education, fostering essential skills like communication, teamwork or decision-making in safe, controlled environments. However, participants’ reflection on simulations often rely on subjective recollections, limiting their effectiveness in promoting learning. This symposium explores how multimodal analytics and AI can enhance simulation-based education by automating teamwork analysis data, providing structured feedback, and supporting reflective practices. The papers examine real-time analytics for closed-loop communication in cardiac arrest simulations, multimodal data use to refine feedback in ICU nursing simulations, generative AI-powered chatbots facilitating nursing students' interpretation of multimodal learning analytics dashboards, and culturally sensitive, AI-based scenarios for Breaking Bad News in an Indian context. Collectively, these contributions highlight the transformative potential of using data and AI-enhanced solutions, emphasizing personalization, cultural sensitivity, and human-centered design, and invite dialogue on the pedagogical, technological and ethical implications of introducing data-based practices and AI-based tools in medical education. 

This NSF-funded study aims to develop and evaluate a novel debriefing system that aims to capture and visualize multimodal data streams from multi-user VR environment that evaluate learners’ cognitive (clinical decision-making) and behavioral (situational awareness, communication) processes to provide data-informed feedback focused on improving team-based care of patients who suffer sudden medical emergencies. Through this new multimodal debriefing system, instructors will be able to provide personalized feedback to clinicians during post-simulation debriefing sessions.