ArticleJournal of medical education and curricular development
A Performance-Based Rubric for Generative AI use in Medical Students' Research Tasks: Development and Initial Psychometric Evaluation.
Article in Journal of medical education and curricular development. 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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10 authors.
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Abstract
Background: As generative AI becomes embedded in medical training, patient safety depends on graduates' ability to recognize AI limitations and bias, document AI involvement transparently, and verify AI-generated information rather than accept it uncritically. We developed a performance-based rubric to assess observable generative AI (LLM) literacy behaviors within authentic coursework. Methods: In a single-institution evaluation (Spring 2025), third-year medical students ( Results: Mean (SD) domain scores were: AI Use Documentation 0.67 (1.08), Prompt Generation 1.33 (0.69), Verification 0.41 (0.71), and Integration 1.64 (0.67); total score 4.06 (1.80). Floor effects were substantial for AI Use Documentation (64% scored 0) and Verification (60% scored 0). Inter-rater reliability was high (ICC: Documentation 0.99, Prompt Generation 0.84, Verification 0.93, Integration 0.83). Verification was significantly lower than Prompt Generation and Integration (Bonferroni-adjusted Conclusions: This rubric demonstrated strong scoring reliability and produced initial psychometric evidence consistent with measuring distinct, observable LLM-use competencies. Findings highlight prominent gaps in verification and transparent documentation, reinforcing competency guidance that emphasizes recognizing AI limitations and verifying AI output to protect patient safety. Further multi-site validation and implementation work is warranted.
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