Background: Biochemistry is a core subject in undergraduate medical training, yet many students find it difficult to understand and retain due to the abstract nature of biochemical pathways and molecular processes. Conventional lecture-based teaching methods may not sufficiently support conceptual learning for all students. In recent years, generative artificial intelligence tools, including Generative Pre-trained Transformer (GPT) models, have been increasingly used by students as supplementary learning aids. Objective: To examine the association between GPT-assisted learning and academic performance, concept retention, and learner satisfaction among undergraduate medical students. Methods: A multicentre retrospective observational study was conducted across medical colleges in Rajasthan, over a one-year period. A total of 150 undergraduate medical students were included and divided into two groups: GPT-assisted learners (n = 75) and conventional learners (n = 75). Academic performance was assessed using internal biochemistry examination scores and concept retention tests, while learner satisfaction was measured using a five-point Likert scale. Statistical analysis included indepe
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