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Teaching Clinical Reasoning in Health Care Professions Learners Using AI-Generated Script Concordance Tests: Mixed

Alexandre Hudon1,2,3,4,5, Véronique Phan6,7, Bernard Charlin5,8

  • 1Department of Psychiatry and Addictology, Faculty of Medicine, Université de Montréal, Pavillon Roger-Gaudry, 2900 Bd Édouard-Montpetit Local L-315, Montréal, QC, H3T 1J4, Canada, 1 514 343 6111.

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Summary
This summary is machine-generated.

Trained artificial intelligence (AI) models can effectively simulate expert judgment for script concordance tests (SCTs), streamlining medical education assessment and providing valuable feedback. This study shows AI

Keywords:
artificial intelligenceclinical reasoningexpert simulationformative assessmentgenerative AIlarge language modelsmedical educationscript concordance test

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Area of Science:

  • Medical Education Technology
  • Artificial Intelligence in Healthcare
  • Clinical Reasoning Assessment

Background:

  • Artificial intelligence (AI) is transforming medical education tools for teaching and assessment.
  • Script concordance tests (SCTs) are effective for evaluating clinical reasoning under uncertainty.
  • Traditional SCTs rely on resource-intensive expert panels for scoring and feedback.

Purpose of the Study:

  • To evaluate the efficacy of large language models (LLMs) in simulating expert judgment for SCTs.
  • To assess AI's capability in authoring, scoring, and providing feedback for cardiology and pneumology SCTs.
  • To gauge student perceptions of SCT difficulty and the pedagogical value of AI-generated feedback.

Main Methods:

  • A cross-sectional, mixed-methods study involving 25 medical students.
  • A 32-item SCT authored by ChatGPT-4o was administered.
  • Six LLMs (trained and untrained) served as simulated experts for scoring and feedback generation.

Main Results:

  • Trained AI models demonstrated significantly higher concordance (ρ=0.64) with student responses compared to untrained models (ρ=0.41).
  • AI-generated feedback was rated as most helpful in 62.5% of cases, particularly from trained models.
  • The SCT showed good internal consistency (Cronbach α=0.76), with moderate perceived difficulty by students.

Conclusions:

  • Trained generative AI models can reliably simulate expert clinical reasoning in SCTs.
  • AI offers a potential solution to streamline SCT design and provide authentic, educationally valuable feedback.
  • Future research should explore AI's longitudinal impact on learning and hybrid human-AI models for medical education.