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Effect of Artificial Intelligence-Augmented Human Instruction on Feedback Frequency and Surgical Performance During
Vanja Davidovic1, Bianca Giglio1, Abdulmajeed Albeloushi2
1Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada.
Journal of Surgical Education
|October 9, 2025
Summary
AI-augmented personalized instruction in surgical simulation reduced feedback frequency and improved technical skills. This indicates fewer errors and enhanced performance in trainees using the NeuroVR simulator.
Area of Science:
- Medical Education
- Surgical Simulation
- Artificial Intelligence
Background:
- Surgical training relies on effective feedback for skill development.
- Artificial intelligence (AI) offers potential for automated feedback in surgical simulation.
- Personalized feedback tailored to trainee errors may optimize learning.
Purpose of the Study:
- To compare AI-augmented personalized instruction with AI tutor and scripted human instruction.
- To determine if AI-augmented personalized instruction reduces feedback frequency and improves surgical skills.
- To assess the impact of AI-driven feedback on trainee performance metrics.
Main Methods:
- A cross-sectional cohort study followed a randomized controlled trial using the NeuroVR surgical simulator.
- Medical students (n=87) were randomized into AI tutor, scripted human, or AI-augmented personalized instruction groups.
- Performance was assessed via feedback frequency and technical skill metrics.
Main Results:
- AI-augmented personalized instruction led to significantly fewer overall and high-force aspirator instructions by the third repetition.
- Compared to AI tutor, AI-augmented personalized instruction reduced healthy tissue removal and improved instrument tip separation.
- All performance metrics showed significant improvement from baseline with AI-augmented personalized instruction.
Conclusions:
- AI-augmented personalized instruction effectively reduces feedback frequency, signaling fewer trainee errors.
- This AI approach significantly enhances simulated surgical skills compared to standard AI tutoring.
- Personalized, AI-driven feedback represents a promising advancement in surgical education.
