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LLM-based pedagogical agent for ICU simulation instructor training: A quasi-experimental study.

Jingbang Liu1, Ting Chen1, Shan Li1

  • 1Nursing Department, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310000, Zhejiang Province, China.

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|November 6, 2025
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Summary

Integrating large language models (LLMs) into Intensive Care Unit (ICU) nursing simulation training improved perceived simulation design and self-efficacy. Usability was comparable to traditional methods, suggesting feasibility for enhanced instructor development.

Keywords:
Generative artificial intelligenceIntensive Care UnitKnowledge baseLarge language modelPedagogical agentSimulated teaching

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Area of Science:

  • Medical Education Technology
  • Artificial Intelligence in Healthcare
  • Nursing Simulation

Background:

  • Intensive Care Unit (ICU) nursing requires advanced skills, with simulation-based training being crucial but limited by cost, time, and faculty capacity.
  • Large language models (LLMs) offer potential to enhance ICU nursing education through rapid scenario generation and on-demand assistance.
  • Limited real-world data exists on the effectiveness and usability of LLM-based agents in ICU instructor training.

Purpose of the Study:

  • To assess the feasibility of incorporating an LLM-based pedagogical agent into simulation instructor training for ICU nurses.
  • To evaluate the impact of LLM integration on learner-perceived simulation design quality and online learning self-efficacy.

Main Methods:

  • An exploratory quasi-experimental study involved 40 ICU nurses, randomly assigned to an LLM agent group (n=20) or traditional blended learning group (n=20).
  • Training effectiveness was measured using the Jeffries Simulation Design Scale (SDS), System Usability Scale (SUS), and Adult Online Learning Self-Efficacy Scale.
  • Data analysis employed Wilcoxon rank-sum tests and t-tests to compare outcomes between groups.

Main Results:

  • The LLM group demonstrated significantly higher scores in case authenticity, scenario complexity, feedback mechanisms, interactivity, and teaching objectives on the SDS.
  • Participants using the LLM agent reported greater self-efficacy in learning ability and learning technology compared to the control group.
  • Teaching satisfaction was high in both groups, with no significant difference in System Usability Scale scores.

Conclusions:

  • Embedding LLM-based pedagogical agents in ICU simulation instructor training is feasible and enhances perceived simulation design and online learning self-efficacy.
  • Usability of LLM agents was comparable to traditional blended learning methods.
  • Further multi-center randomized controlled trials are needed to confirm efficacy and the independent contribution of LLMs.