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Effect of Artificial Intelligence-Augmented Human Instruction on Surgical Simulation Performance

NCT06273579 · Status: COMPLETED · Phase: NA · Type: INTERVENTIONAL · Enrollment: 88

Last updated 2025-05-30

No results posted yet for this study

Summary

At the Neurosurgical Simulation and Artificial Intelligence Learning Centre, we seek to provide surgical trainees with innovative technologies that allow them to improve their surgical technical skills in risk-free environments, potentially improving patient operative outcomes. The Intelligent Continuous Expertise Monitoring System (ICEMS), a deep learning application that assesses and trains neurosurgical technical skill and provides continuous intraoperative feedback, is one such technology that may improve surgical education.

In this randomized controlled trial, medical students from four Quebec universities will be blinded and randomized to one of three groups (one control and two experimental). Group 1 (control) will be provided with verbal AI tutor feedback based on the ICEMS error detection. Group 2 will be tutored by a human instructor who will receive ICEMS error data and deliver verbal instruction using the same words as the ICEMS. Group 3 will be tutored by a human instructor who will be provided with ICEMS data and will then deliver personalized feedback.

The aim of this study is to determine how the method of delivery of verbal surgical error instruction influences trainee technical skill acquisition and transfer. Evaluating trainee responses to AI instructor verbal feedback as compared to feedback from human instructors will allow for further development, testing, and optimization of the ICEMS and other AI tutoring systems.

Conditions

  • Surgical Education

Interventions

BEHAVIORAL

Expert instruction using AI tutor script

Expert instructor assigned to tutor this group will receive error detection data from the ICEMS. They will also be provided with a list of commands that the ICEMS uses. When the system detects an error in a student's performance for a given metric, the instructor must deliver this command using the exact wording provided by the ICEMS.

BEHAVIORAL

AI-augmented personalized expert instruction

Expert instructor assigned to tutor this group will receive error detection data from the ICEMS. When the system detects an error in a student's performance for a given metric, the instructor will have the freedom to personalize and contextualize feedback without restriction to ICEMS wording.

Sponsors & Collaborators

Study Design

Allocation
RANDOMIZED
Purpose
HEALTH_SERVICES_RESEARCH
Masking
SINGLE
Model
PARALLEL

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2024-03-09
Primary Completion
2024-09-14
Completion
2024-09-14

Countries

  • Canada

Study Locations

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Entities

Companies

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06273579 on ClinicalTrials.gov