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Exploring the Use of a Large Language Model in Simulation Debriefing: An Observational Simulation-Based Pilot Study.

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Summary
This summary is machine-generated.

Generative artificial intelligence (AI) can support simulation debriefings by organizing complex information, potentially enhancing learning quality. While facilitators experienced moderate to high task load, AI integration showed promise in improving focus and minimizing distractions during simulation-based education.

Keywords:
Simulationartificial intelligence (AI)debriefinglarge language model (LLM)

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Area of Science:

  • Medical Simulation
  • Artificial Intelligence in Education
  • Human-Computer Interaction

Background:

  • Simulation-based education requires effective debriefing, which is a complex task with high facilitator workload.
  • Generative artificial intelligence (AI) presents an opportunity to support simulation facilitators and enhance debriefing quality.
  • Large language models (LLMs) can potentially decrease facilitator task load and enable more comprehensive debriefs.

Purpose of the Study:

  • To explore the use of a large language model (LLM) for facilitating simulation debriefings.
  • To assess the impact of LLM integration on facilitator task load and simulation debriefing quality.
  • To evaluate learner and facilitator perceptions of AI technologies in simulation-based education.

Main Methods:

  • A prospective, observational, simulation-based pilot study at Yale University School of Medicine.
  • Real-time simulation transcriptions were input into the GPT-4o LLM to generate debriefing scripts.
  • Facilitator and learner task workload (NASA-TLX) and perceptions of AI were assessed via surveys.

Main Results:

  • Four facilitators and 25 learners participated; all data were self-reported.
  • Participants showed strong enthusiasm for AI integration (mean Likert scores 4.75/5 for facilitators, 4.0/5 for learners).
  • AI was perceived to help maintain focus (4.8/5), support learning objectives (4.2/5), and minimize distractions (4.6/5). Facilitators and learners reported moderate to high mental demand (NASA-TLX scores 10.8/21 and 9.9/21).

Conclusions:

  • LLM integration aids debriefing by organizing complex simulation information.
  • LLMs show potential to enhance simulation-based debrief quality, despite reported facilitator task load.
  • Continuous human oversight is essential when implementing AI in simulation debriefings.