Abstract

Abstract Aims An artificial intelligence-driven tutoring system provides risk-free training on realistic simulated procedures and objective assessment to improve surgical technical skills. This study compared its teaching efficacy to human instructor training. Methods Ninety-eight medical students completed 5 simulated brain tumor resections, scored by the intelligent system. Participants were randomly sorted into 3 feedback groups: (1) no real-time feedback, (2) real-time intelligent instruction, (3) real-time human instruction. Each participant completed a base-line repetition without feedback. Group 1 received visual feedback based on expert benchmarks following each repetition. Group 2 was tutored by the intelligent system in real-time. After each repetition, the system provided the student’s error clips and correlating expert-level demonstrations. Group 3 was tutored by human instructors in real-time. After each repetition, instructors provided critiques and demonstrated correction techniques. Following the final repetition, each participant completed a questionnaire of Leppink’s Cognitive Load Index on a 5-point Likert scale. Performance scores were compared from the first to the last repetition of the task within and between-groups to assess learning. Results Compared with baseline, groups 2 and 3 significantly improved in performance by the third and second repetition, respectively (p<0.01 and p=0.01). Between-groups comparison revealed significantly higher scores and extrinsic load for group 2 than group 3 in the fifth repetition, respectively (p<0.01 and p<0.01). No between-group differences existed for intrinsic and germane load. Conclusion The intelligent system provided more efficient learning than human instruction. Such systems may enhance trainees’ learning and facilitate the shift towards standardized, competency-based surgical training.

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