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Generative AI-Assisted Personalized Feedback for Laparoscopic Skill Learning in Medical Undergraduates
Sponsor: Yang Xiaodong
Summary
Laparoscopic training involves learning to control surgical instruments while viewing movements on a screen. Feedback can help learners identify technical problems and plan further practice, but providing individualized guidance can require substantial instructor time. This study compares personalized feedback assisted by generative artificial intelligence (AI) with feedback produced using predefined rules and fixed statements. The purpose is to determine whether AI assistance provides additional benefits for laparoscopic skills learning. The investigators hypothesize that AI-assisted personalized feedback will help medical students improve laparoscopic skills more than structured rule-based feedback.
Official title: Effect of Generative Artificial Intelligence-Assisted Personalized Feedback on Laparoscopic Skill Learning in Medical Undergraduates: A Randomized Controlled Trial
Key Details
Gender
All
Age Range
18 Years - Any
Study Type
INTERVENTIONAL
Enrollment
40
Start Date
2026-10-15
Completion Date
2027-04-30
Last Updated
2026-10-30
Healthy Volunteers
Yes
Conditions
Interventions
Generative AI-Assisted Personalized Feedback
This intervention consists of generative artificial intelligence-assisted personalized feedback provided during laparoscopic skills training. The feedback is generated through a study-specific AI workflow using de-identified structured training data, including objective task performance measures, error events, technical assessment scores, and self-assessment information. The AI-generated feedback identifies individualized training priorities, provides practice suggestions, and defines observable goals for subsequent training. All AI-generated feedback undergoes predefined quality review before delivery. Participants receive four scheduled feedback events during the training period.
Structured Template-Based Feedback
This intervention consists of structured template-based feedback provided during laparoscopic skills training. Feedback is generated using a predefined rule-based system with fixed criteria, priorities, target conditions, and feedback statements. The template system uses the same categories of structured training data and the same feedback schedule as the generative AI-assisted feedback group. Participants receive four scheduled feedback events during the training period.
Locations (1)
The Second Hospital of Shandong University
Jinan, Shandong, China