Evaluating large language models as graders of medical short answer questions: a comparative analysis with expert human graders
The assessment of short-answer questions (SAQs) in medical education is resource-intensive, requiring significant expert time. Large Language Models (LLMs) offer potential for automating this process, but their efficacy in specialized medical education assessment remains understudied. To evaluate th...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2025-12-01
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| Series: | Medical Education Online |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/10872981.2025.2550751 |
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