AI integration in healthcare simulation debriefing: support or substitute?

Debriefing is widely recognised as a central mechanism for learning within healthcare simulation, enabling learners to reflect on clinical actions, decision-making, and team interactions. However, high-quality debriefing is resource-intensive, dependent on facilitator expertise, and increasingly challenged by the growing complexity and volume of data generated during modern simulation activities. Artificial intelligence (AI) offers emerging opportunities to augment aspects of debriefing by analysing performance data, structuring reflective dialogue, and supporting learning environments. This article explores the emerging role of AI within debriefing. Drawing on the current literature, we describe four modes of AI being integrated into debriefing practice: metric-based AI tutors, large language model-assisted debriefing tools, conversational chatbot debriefers and hybrid integrated AI systems. For each mode, we examine their underlying mechanisms, current applications, and current contributions to, and limitations within, debriefing. Using these four modes as a scaffold, we offer practical guidance for simulation practitioners considering the integration of AI tools within their own practice, including considerations related to faculty AI literacy, educational alignment, governance, and implementation. While the empirical evidence base is evolving, AI-driven approaches offer new ways of supporting facilitators in augmenting reflective practice. When implemented thoughtfully and with appropriate human oversight, the integration of AI into debriefing portends a new era supporting reflective learning within healthcare simulation.