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http://hdl.handle.net/11054/3016| Title: | Large language model-supported interactive case-based learning: a pilot study. |
| Author: | Gim, H. Cook, B. Le, J. Stretton, B. Gao, C. Gupta, A. Kovoor, Joshua Guo, C. Arnold, M. Gheihman, G. Bacchi, S. |
| Issue Date: | 2025 |
| Publication Title: | Internal Medicine Journal |
| Volume: | 55 |
| Issue: | 5 |
| Start Page: | 852 |
| End Page: | 855 |
| 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. |
| URI: | http://hdl.handle.net/11054/3016 |
| DOI: | https://doi.org/10.1111/imj.70030 |
| Internal ID Number: | 02963 |
| Health Subject: | ARTIFICIAL INTELLIGENCE MACHINE LEARNING NATURAL LANGUAGE PROCESSING MEDICAL EDUCATION CLINICAL REASONING |
| Type: | Journal Article Article |
| Appears in Collections: | Research Output |
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