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AI integration in healthcare simulation debriefing: support or substitute?
Lucy Stocks1,2, Marc Place3, Victoria Brazil4
1Department of Medical Education, NHS Lanarkshire, Bothwell, Scotland, UK. Lucy.Stocks@lanarkshire.scot.nhs.uk.
Advances in Simulation (London, England)
|June 11, 2026
Summary
Artificial intelligence (AI) can enhance healthcare simulation debriefing by analyzing data and structuring dialogue. Thoughtful integration of AI tools supports reflective practice and learning, even as evidence evolves.
Area of Science:
- Healthcare Simulation
- Medical Education Technology
- Artificial Intelligence in Medicine
Background:
- Debriefing is crucial for learning in healthcare simulation, but is resource-intensive and faces challenges with complex data.
- Artificial intelligence (AI) presents opportunities to improve debriefing by analyzing performance data and supporting reflective learning.
Purpose of the Study:
- To explore the emerging role of AI in healthcare simulation debriefing.
- To describe four modes of AI integration and provide practical guidance for practitioners.
Main Methods:
- Literature review to identify and categorize AI applications in debriefing.
- Analysis of four AI modes: metric-based AI tutors, LLM-assisted tools, chatbot debriefers, and hybrid systems.
- Examination of mechanisms, applications, contributions, and limitations of each AI mode.
Main Results:
- Four distinct modes of AI integration in debriefing are identified and described.
- Current applications, benefits, and limitations of each AI mode are discussed.
- Practical considerations for implementing AI in simulation debriefing are outlined.
Conclusions:
- AI-driven approaches offer novel ways to support facilitators and enhance reflective practice in healthcare simulation.
- Thoughtful implementation with human oversight can usher in a new era of AI-supported reflective learning.
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