Prospective observational study demonstrates the integration of AI patient cases in preclinical medical training, indicating enhanced engagement and scalable neuroscience learning.
Case-based learning is central to neuroscience education, yet traditional approaches provide limited opportunities for iterative, interactive practice and individualized feedback among large preclinical cohorts. Large language models (LLMs) may support case-based learning by enabling interactive patient simulations and scaling formative feedback. Feasibility studies have shown high accuracy and minimal hallucinations; however, few have examined their real-world integration within medical school courses. We introduced AI-enabled cases as consolidation exercises in a preclinical neurosciences course. In this prospective pre-post observational study, we evaluated learner engagement, perceived educational value, and compared examination performance of students with access to AI-enabled cases compared with the prior year.
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Kaplan et al. (2026) studied this question.
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