AI-Generated and Rule-Based Individualized Feedback for Medical Students' Nephrology Learning

Medical education increasingly emphasizes individualized learning and formative assessment; however, providing timely and personalized feedback to students remains challenging because of limited faculty resources. Generative artificial intelligence (AI) may provide a scalable approach for generating individualized feedback based on students' learning needs, but its educational effectiveness compared with traditional feedback approaches remains uncertain. This study is a single-center, prospective, three-arm, cluster randomized controlled trial designed to evaluate the effect of pretest-based individualized feedback on medical students' learning outcomes during a nephrology clerkship. Undergraduate medical students participating in a nephrology clerkship at The Second Qilu Hospital of Shandong University will be recruited and assigned by teaching class clusters to one of three groups: an AI-generated feedback group, a rule-based individualized feedback group, or a no-feedback control group. All participants will receive the same nephrology clerkship teaching based on the BOPPPS-CBL instructional model. Before the clerkship, students will complete a diagnostic pretest. Based on the pretest results, individualized feedback will be provided according to group allocation. The primary outcome is immediate knowledge acquisition assessed by the post-test score immediately after the clerkship. Secondary outcomes include four-week knowledge retention, knowledge gap remediation, and clinical reasoning performance. This study aims to determine whether AI-generated individualized feedback provides additional educational benefits compared with traditional rule-based feedback and no feedback, and to provide evidence for the application of artificial intelligence-assisted personalized learning in medical education.