SP11.14 Evaluating the Efficacy and Cognitive Load of a Real-Time Intelligent Coaching System versus Human Expert Instruction in Surgical Technical Skills Training: A Randomized Controlled Study
Bibliographic record
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".