From Lectures to Learning Outcomes: Meaningful Integration of AI-Generated Content in Pre-Clerkship Medical Training

Abstract

Abstract Large Language Models (LLMs) have shown considerable promise in knowledge processing and synthesis across various medical disciplines. In medical education, most applications have focused on comparing LLM outputs to trainee performance or using LLMs for standardized assessment. However, few studies have systematically evaluated the effects of standardized, LLM-powered, curricular interventions on medical learning. This case study, conducted at The Warren Alpert Medical School of Brown University, assessed the impact of AI-generated Anki flashcards and lecture summaries specifically optimized for the pre-clerkship phase. These materials were developed using a rigorous, specific, and content-agnostic prompt engineering process and validated through standardized human grading to ensure both accuracy and relevance. The final prompts used demonstrated hallucination rates of 0 per summary and 1 per 21 flashcards and average coverage of 100% of faculty-identified learning objectives. Materials were given to students for two 3-week academic blocks, covering genetics and pharmacology. Student exam scores and survey-based feedback were used to evaluate the effectiveness of these AI-generated resources. The study was conducted in a resource-rich pre-clerkship setting where students already have access to faculty-created materials, commercial content, and student-curated resources. We aimed to determine whether AI-generated content could offer measurable quantitative improvements or subjective qualitative benefits in a saturated learning environment. Among participating first-year medical students, overall exam performance between those who used the AI-generated summaries and those who did not was comparable in both the genetics block (p = 0.76) and the pharmacology block (p = 0.35). Similarly, use of the AI-generated Anki flashcards was not associated with significant differences in exam scores for either genetics (p = 0.86) or pharmacology (p = 0.05). Qualitative analyses demonstrated widespread time saving for Anki flashcards (74%) and AI-generated summaries (61%), with 91% of users finding the custom AI-generated content more time-saving than default GPT-4o. There was a significant usage-dependent relationship of higher AI-usage correlating with increased agreement of equivalency or utility over faculty-generated lecture notes (Pearson’s r 2 =0.55) and student-created flashcards (Pearson’s r 2 =0.79). These findings suggest that students who used AI-generated content maintained comparable educational outcomes in the pre-clerkship setting. Moreover, subjective perceptions among learners, such as time saved and content usefulness, highlight the potential value of LLM-powered tools when layered on top of an existing well-resourced curricular structure. Future work will examine the benefits of this work in less structured medical education settings, such as clinical and surgical education.

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