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

Condition(s) studied

Investigational drug(s) / intervention(s)

AI-Generated Individualized Feedback: Participants receive individualized feedback generated using generative artificial intelligence based on their pretest performance. Before the nephrology clerkship, students complete a diagnostic pretest. Their incorrect responses and knowledge gaps are analyzed, and AI-generated feedback is produced using a structured prompt framework. The generated feedback includes identified learning gaps, explanations, and targeted learning recommendations. All AI-generated feedback is reviewed by nephrology faculty before being provided to students.

Rule-Based Individualized Feedback: Participants receive individualized feedback generated using a predefined rule-based feedback system based on their pretest performance. The system links test items, knowledge domains, error types, and corresponding feedback modules to generate targeted learning recommendations. Feedback content is reviewed before being provided to students. Participants subsequently receive the same nephrology clerkship teaching as other study groups.

No Individualized Feedback: Participants complete the diagnostic pretest but do not receive individualized feedback, explanations, or learning recommendations based on their test results before the nephrology clerkship. They receive the same BOPPPS-CBL nephrology clerkship teaching as participants in the other study groups.

Study summary

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.

Eligibility

Primary outcome measure(s)

• Immediate Knowledge Acquisition — Immediately after completion of the nephrology clerkship
  Immediate knowledge acquisition will be assessed using the total score of the post-intervention nephrology knowledge test (B questionnaire), administered immediately after completion of the nephrology clerkship. The B questionnaire is a parallel-form knowledge assessment based on the same predefined assessment blueprint as the baseline and delayed tests. The total score is converted to a 0-100 scale, with higher scores indicating greater knowledge acquisition.

Trial sites (1)

Other trials for the same condition