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Updated: Jan 31, 2026

Setting Up a Stroke Team Algorithm and Conducting Simulation-based Training in the Emergency Department - A Practical Guide

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Published on: January 15, 2017

Generative AI in simulation debriefings: an exploratory study using the Team-FIRST framework and qualitative feedback

David W Tscholl¹, Max Ebensperger², Arend RahrischRahrisch¹

¹Institute for Anesthesiology and Perioperative Medicine, University Hospital Zurich, University of Zurich, Zurich, Switzerland.

Advances in Simulation (London, England)

|January 30, 2026

Summary

Generative artificial intelligence (AI) can aid medical simulation debriefings by analyzing team communication and providing feedback. However, human oversight is crucial due to AI

Keywords:

Automated assessments Clinical education technology Debriefing Generative artificial intelligence Healthcare Large language models Qualitative thematic analysis Simulation Teamwork

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Published on: September 18, 2012

Area of Science:

Medical Education Technology
Artificial Intelligence in Healthcare
Simulation-Based Training

Background:

Simulation-based education requires effective debriefings for optimal learning.
Facilitators face challenges in observing team interactions due to cognitive load and bias.
Generative AI offers potential to analyze communication and support debriefing in medical simulations.

Purpose of the Study:

To explore the utility of generative AI tools in observing teamwork during medical simulations.
To assess the impact of AI-generated reports on simulation debriefing processes.
To understand participants' and facilitators' experiences with AI in simulation.

Main Methods:

Qualitative exploratory study involving thematic analysis of AI-generated reports and interviews.
Forty-one participants in immersive medical simulations at University Hospital Zurich.
AI-assisted transcription and analysis of verbal interactions using large language models (Isaac, ChatGPT-4o) based on the Team-FIRST framework.

Main Results:

AI reports provided detailed transcripts and quotes, aiding structured feedback and capturing missed observations.
Limitations included categorization inaccuracies, speaker misattribution, and lack of nonverbal context.
Learners were optimistic about AI's efficiency and objectivity but concerned about transparency and errors; human oversight was deemed essential.

Conclusions:

Generative AI can enhance simulation debriefings by structuring communication data and identifying teamwork patterns.
Current AI limitations necessitate multimodal approaches and expert facilitation.
AI should serve as a supportive tool, not a replacement, in simulation-based education.