Applications of Artificial Intelligence for Nonpsychomotor Skills Training in Health Professions Education: A Scoping Review
Bibliographic record
Abstract
PURPOSE: This study explores uses of artificial intelligence (AI) in health professions education for nonpsychomotor skills training at undergraduate, postgraduate, and continuing health professions education levels for education program development, delivery, and evaluation. METHOD: This scoping review was conducted in 5 stages: (1) planning and research, (2) search strategy, (3) screening and selection, (4) review and recording data, and (5) synthesis. Seven bibliographic databases were searched using terms for artificial intelligence and continuing health professional education to capture articles that used AI for the purposes of nonpsychomotor skills training for health professions education and involved health care professionals and/or trainees. Databases were searched for articles published from January 1, 2001, to March 26, 2024. The original searches were performed on July 26, 2021, and again on March 26, 2024. Two reviewers independently screened, reviewed, and extracted data. Data extraction was performed using Kern's 6-step curriculum development framework to guide analysis. RESULTS: In total, 9,914 studies related to AI in health professions education for nonpsychomotor skills training were screened. Of these, 103 studies were identified that met the inclusion criteria. Of these 103 studies, 52 (50%) were cohort studies. The most common learner population was health care professional students (67 studies [65%]). Most studies (76 [74%]) were set in nonclinical settings. Sixty-eight studies (66%) fit under step 6 of Kern's criteria (evaluation and assessment), illustrating that AI is predominantly being used for the purposes of evaluation and assessment of learners and programs. CONCLUSIONS: Most studies in the literature illustrate that AI is being applied in a nonpsychomotor context to evaluate health professional education programs and assess learners. Additional opportunities to use AI in curriculum design and implementation could include identification of learning needs for training, personalizing learning with AI principles, and evaluating health care professional education programs.
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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 | 0.032 | 0.112 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.029 | 0.029 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".