Applications of Artificial Intelligence for Nonpsychomotor Skills Training in Health Professions Education: A Scoping...

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.

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.

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.

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.