Get started
Work

Large language model‐supported interactive case‐based learning: a pilot study

Year: 2025
Type: article
Abstract: Large language models (LLMs) have been proposed as a means to augment case-based learning but are prone to generating factually incorrect content. In this study, an LLM-based tool was developed, and its performance evaluated. In response to student-generated questions, the LLM adhered to the provided screenplay in 832/857 (97.1%) instances, and in the remaining instances, it was medically appropriate in 24/25 (96.0%) cases. Use of LLM appears to be feasible for this purpose, and further studies are required to examine their educational impact.
DOI: 10.1111/imj.70030
Authors: Haelynn Gim, Benjamin Cook, Jasmin Le, Brandon Stretton, Christina Gao, Aashray Gupta, Joshua Kovoor, Christina Guo, Matthew Arnold, Galina Gheihman, Stephen Bacchi
Institutions: Harvard University, The University of Adelaide, Ballarat Health Services, Johns Hopkins University, The Alfred Hospital, Mass General Brigham, Flinders University, Lyell McEwin Hospital, Massachusetts General Hospital
Language: English
Field: Medicine
Open Access status: bronze
OpenAlex ID: W4408785989
FWCI: 1.428
Cites: 13
Cited by: 7
Related to: 10