Use of Large Language Models for Rapid Quantitative Feedback in Case-Based Learning: A Pilot Study
Author(s)
Qian, Carolyn; Gao, Christina; Park, Sang-O.; Gim, Haelynn; Hou, Kelly; Cook, Benjamin; Le, Jasmin; Stretton, Brandon; Maddison, John; McCoy, Liam; Goh, Rudy; Arnold, Matthew; Reda, Haatem; Kaplan, Tamara; Gheihman, Galina; ... Show more Show less
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Show full item recordAbstract
Abstract Large language models (LLMs) may be able to deliver interactive case-based content and score student interactions with such cases. In this study, GPT-4o demonstrated a high correlation with expert scorers in the evaluation of medical students’ interactions with cases. A difference between LLM scores and expert scorers was corrected through calibration.
Date issued
2025-02-28Department
Institute for Medical Engineering and ScienceJournal
Medical Science Educator
Publisher
Springer US
Citation
Qian, C., Gao, C., Park, SO. et al. Use of Large Language Models for Rapid Quantitative Feedback in Case-Based Learning: A Pilot Study. Med.Sci.Educ. 35, 1169–1171 (2025).
Version: Author's final manuscript