Feasibility and Perceived Impact of an AI-Assisted Pre-Matriculation Study Strategies Curriculum for Incoming Medical Students

Background Many incoming medical students begin training using study approaches that were effective in prior academic settings but may not meet the pace and cognitive demands of medical school. Evidence-based strategies such as retrieval practice and spaced repetition improve long-term retention, yet students are rarely formally taught how to apply them. Large language models (LLMs) may offer a scalable way to develop accessible educational materials that introduce these concepts to students before matriculation. Objective The objective of this study was to evaluate the feasibility, engagement, and perceived impact of an eight-week, AI-assisted "study strategies" curriculum for incoming medical students. Methods All incoming students at a single medical school received weekly emails summarizing evidence-based study techniques, with supplementary PDF summaries and podcasts. Content was drafted using LLMs and reviewed for accuracy. Students completed a voluntary survey assessing demographics, prior instruction on study strategies, engagement with materials, and perceived impact. Descriptive statistics summarized outcomes. Group comparisons used t-tests and ANOVA, and Pearson correlations assessed relationships between engagement and perceived impact. Results A total of 65% (78/120) students completed the survey. Students read a mean of 3.58 emails (SD=2.53), reviewed 2.29 PDFs (SD=2.22), and listened to 1.03 podcasts (SD=1.76). Participants reported learning new techniques they planned to use (M=2.09, SD=0.63), moderate influence on study approach (M=2.58, SD=0.85), and moderate likelihood of applying strategies in medical school (M=3.10, SD=1.14). Greater email engagement correlated with perceived helpfulness, learning new techniques, influence on study approach, and likelihood of application (r=0.302-0.612, all p<0.01). No significant differences were observed by first-generation status, prior formal instruction, or path to medical school. Conclusion This AI-assisted pre-matriculation study strategies curriculum was feasible to implement and was perceived as useful by participating students. Higher engagement with curricular materials was associated with greater perceived impact on study approaches and planned application of evidence-based learning strategies.