ArticleJournal of microbiology & biology education2026
Automated scoring of student videos in medical education: a comparison between a large language model and expert evaluation.
Article in Journal of microbiology & biology education, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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4 authors.
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Abstract
Video-based assignments are used in medical education, yet expert scoring is time-intensive. Large language models (LLMs) offer scalable alternatives, but their validity for evaluating multimodal student work is uncertain. We examined whether a state-of-the-art LLM (Gemini 2.5 Pro) could approximate expert scoring of medical student video presentations in evidence-based medicine. A total of 139 student submissions were evaluated by an experienced faculty member (reference standard) and by the LLM under two prompting strategies: (i) rubric-only and (ii) critical expert-style. Scores across 12 rubric items (maximum 95 points) were compared using paired
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