AI’s Accuracy in Extracting Learning Experiences From Clinical Practice Logs: Observational Study
This study demonstrated that LLMs such as GPT-4-turbo can predict clinical experiences from learning logs with high specificity but moderate sensitivity. Future improvements in AI models, providing feedback to medical students' learning logs and combining them with other data sources such as electronic medical records, may enhance the accuracy. Using artificial intelligence to analyze learning logs for assessment could reduce the burden on learners and educators while improving the quality of educational assessments in medical education.
Authors
- Hiroshi Nishigori (ORCID: https://orcid.org/0000-0002-0715-7073)
- Takeshi Kondo (ORCID: https://orcid.org/0000-0002-3307-671X)
Institutions
- Nagoya City University (JP)
- Maastricht University (NL)
- Nagoya University (JP)
Publication Details
- Journal
- JMIR Medical Education
- Published
- 2025-09-23
- DOI
- https://doi.org/10.2196/68697
- Citations
- 1
- Primary Topic
- Intelligent Tutoring Systems and Adaptive Learning
- Type
- article
- Field-Weighted Citation Impact
- 2.48