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Related Concept Videos

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Naturalistic Observations

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If you want to understand how behavior occurs, one of the best ways to gain information is to simply observe the behavior in its natural context. However, people might change their behavior in unexpected ways if they know they are being observed. How do researchers obtain accurate information when people tend to hide their natural behavior? As an example, imagine that your professor asks everyone in your class to raise their hand if they always wash their hands after using the restroom. Chances...
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Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
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Related Experiment Video

Updated: Jan 15, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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AI's Accuracy in Extracting Learning Experiences From Clinical Practice Logs: Observational Study.

Takeshi Kondo1,2, Hiroshi Nishigori1

  • 1Center for Medical Education, Nagoya University Graduate School of Medicine, 65, Tsurumai-cho, Showa-ku, Nagoya city, Aichi, 466-8560, Japan, +81 052 7412111.

JMIR Medical Education
|October 15, 2025
PubMed
Summary

Large language models (LLMs) can predict medical students' clinical experiences from learning logs with high accuracy, though some details may be missed. This AI application shows promise for reducing educator burden and enhancing medical education assessment.

Keywords:
AIChatGPTartificial intelligencelarge language modelsworkplace-based assessment

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Area of Science:

  • Medical Education Technology
  • Artificial Intelligence in Healthcare
  • Clinical Learning Analytics

Background:

  • Improving clinical education requires understanding student experiences, but manual analysis is burdensome.
  • Automated analysis of learning records and visualization of experiences can enable real-time progress tracking.
  • Large language models (LLMs) show potential for analyzing clinical learning data, but their accuracy needs evaluation.

Purpose of the Study:

  • To assess the accuracy of LLMs in predicting medical students' actual clinical experiences from learning log data.
  • To explore the utility of LLMs for real-time progress tracking in clinical clerkships.

Main Methods:

  • Learning log data from medical students at Nagoya University School of Medicine were analyzed.
  • OpenAI's ChatGPT, specifically GPT-4-turbo, was used to extract experiences based on the Model Core Curriculum for Medical Education.
  • A web application was developed to automate the extraction process, with accuracy evaluated against student-provided corrected lists.

Main Results:

  • The study involved 20 sixth-year medical students, yielding 40 datasets.
  • GPT-4-turbo demonstrated high overall specificity (99.34%) but moderate sensitivity (62.39%) in predicting clinical experiences.
  • Performance varied by category, with lower sensitivity for symptoms (45.43%) and examinations (46.76%) compared to procedures (56.36%).

Conclusions:

  • LLMs like GPT-4-turbo can predict clinical experiences from learning logs with high specificity and moderate sensitivity.
  • Future enhancements may involve improved AI models, feedback mechanisms for learning logs, and integration with electronic medical records.
  • AI-driven analysis of learning logs can potentially reduce assessment burdens and improve the quality of medical education.