Artificial Intelligence-Assisted Versus Traditional Learning and Long-Term Knowledge Retention Among Undergraduate Medical Students: A Sequential, Explanatory Mixed-Methods Study.

Background Artificial intelligence (AI) is increasingly being integrated into medical education to support self-directed learning through personalized explanations and immediate feedback. Although AI has been shown to improve learning outcomes, its effect on long-term memory retention remains unclear. This study compared long-term memory retention following AI-assisted learning and traditional learning among undergraduate medical students and explored students' perceptions regarding AI-assisted learning. Methods A prospective, within-subject, sequential, explanatory mixed-methods study was conducted among 149 second-year MBBS students at Kanyakumari Government Medical College, Tamil Nadu, India. Students completed two learning sessions: traditional learning (aminoglycosides) and AI-assisted learning (quinolones), each consisting of a pre-test, a 15-minute introductory lecture, one hour of self-directed learning, and an immediate post-test. Knowledge retention was assessed using parallel Very Short Answer Question (VSAQ) tests at one week and one month. Quantitative data were analyzed using paired t-tests and repeated-measures analysis of variance (ANOVA). Subsequently, focus group discussions were conducted among students with high and low retention scores, and qualitative data were analysed using thematic analysis. Results Both learning methods significantly improved immediate post-test scores. However, AI-assisted learning was associated with significantly higher knowledge retention at 1 week (75.5% vs. 57.9%) and 1 month (61.4% vs. 35.9%) compared with traditional learning (p<0.001). Repeated-measures ANOVA showed significant effects of learning method (F=181.66, p<0.001), assessment time (F=625.73, p<0.001), and their interaction (F=62.23, p<0.001). A significantly greater proportion of students exceeded a Bloom-inspired heuristic benchmark for high memory retention following AI-assisted learning (43.4% vs. 3.0%). This benchmark was conceptual and exploratory rather than an exact replication of Bloom's original Two Sigma methodology. Of 149 enrolled students, 115 attended the pre-test in both modalities and formed the base analytic cohort; 95 students (82.6% of this cohort) had complete data across all 4 assessment waves, were included in the repeated-measures analysis, and did not differ from non-completers on baseline scores. Four themes emerged from the qualitative analysis: AI as a personalized tutor, enhanced conceptual understanding, visual learning and memory retention, and cognitive offloading. Conclusion AI-assisted learning was associated with higher long-term memory retention compared with traditional learning under the specific, non-randomized conditions investigated; this association does not establish that AI-assisted learning caused the improvement. Students' perceptions of personalized, interactive explanations and active engagement appeared linked to improved retention, whereas passive reliance on AI-generated responses was associated with cognitive offloading. Confirmation of these associations, and of any causal role for AI-assisted learning, will require randomized crossover trials using equivalent topics, standardized AI platforms, and validated assessments.