Mastery Learning Guided by Artificial Intelligence Is Superior to Directed Self-Regulated Learning in Flexible Bronchoscopy Training: An RCT.

Abstract

Introduction: Simulation-based training has proven effective for learning flexible bronchoscopy. However, no studies have tested the efficacy of training toward established proficiency criteria, i.e., mastery learning (ML). We wish to test the effectiveness of ML compared to directed self-regulated learning (DSRL) on novice bronchoscopists' end-of-training performance.

Methods: In a standardized simulated setting, novices without prior bronchoscopy experience were trained using an artificial intelligence (AI) guidance system that automatically recognizes the bronchial segments. They were randomized into two groups: the ML group and the DSRL group. The ML group was trained until they completed two procedures meeting the proficiency targets: 18 inspected segments, 18 structured progressions, <120-s procedure time. The DSRL group was trained until they no longer perceived any additional benefits from training. Both groups then did a finalizing test, without the AI guidance enabled.

Results: A total of 24 participants completed the study, with 12 in each group. Both groups had a high mean number of inspected segments (ML = 17.2 segments, DSRL = 17.3 segments, p = 0.85) and structured progressions (ML = 15.5 progressions, DSRL = 14.8 progressions, p = 0.58), but the ML group performed the test procedure significantly faster (ML = 107 s, DSRL = 180 s, p < 0.001). The ML did not spend significantly longer time training (ML = 114 min, DSRL = 109 min, p = 0.84).

Conclusions: ML is a very efficient training form allowing novice trainees to learn how to perform a thorough, systematic, and quick flexible bronchoscopy. ML does not require longer time spent training compared to DSRL, and we therefore recommend training of future bronchoscopists by this method.