Developing Automated Software for Improving Feedback Analysis and Self-Debriefing in High-Fidelity Medical Simulation

Simulation in medical education bridges gaps in clinical exposure, using high-fidelity manikins for immersive training in procedures like Basic Life Support (BLS) and cardiopulmonary resuscitation (CPR). A high-fidelity simulation includes pre-briefing, clinical scenarios, and debriefing. The debriefing process provides feedback to help learners identify knowledge gaps and improve performance. However, existing manikin systems feedback does not always align with simulation objectives, educator expectations, and the latest guidelines. To address this, we developed automated feedback software guided by medical standards and expert input, generating detailed reports on student actions. We propose using automated feedback and video recordings to enhance self-debriefing.