Self-Reported AI Competency and Educational Needs for Integrated AI and Digital Rehabilitation Education Among Occupational Therapy Students: A Cross-Sectional Survey.
- 1Department of Occupational Therapy, Choonhae Health Sciences University, Ulsan, Republic of Korea.
- 2Department of Occupational Therapy, College of Health & Medical Sciences, Cheongju University, Cheongju, 28503, Republic of Korea.
Abstract
The growing use of generative artificial intelligence (AI) in healthcare calls for occupational therapy education that combines critical appraisal, clinical reasoning, and digital rehabilitation. This study aimed to examine students' interest in AI, perceived AI competency, and needs for integrated AI and digital rehabilitation education, and identified factors associated with educational content needs. An exploratory cross-sectional survey using convenience sampling and five researcher-developed scales without full psychometric validation was conducted among occupational therapy students from three universities. A total of 250 questionnaires were submitted, and 230 complete responses were analyzed. Descriptive statistics, reliability tests, Welch group comparisons, Pearson correlations, and hierarchical multiple regression with sensitivity analyses were used. Mean scores were 3.64±0.77 for AI interest, 3.42±0.67 for self-reported AI competency, 3.93±0.68 for educational content needs, 3.95±0.63 for educational priority, and 3.82±0.64 for instructional method preference. All five domains were positively correlated. Educational priority and instructional method preference showed the strongest correlation (r=0.701). AI interest (β=0.490, p<0.001) and self-reported AI competency (β=0.154, p=0.015) were associated with educational content needs in the conventional regression; the final model explained 34.3% of the variance. The competency association was sensitive to robust inference (HC3 p=0.0504). Students expressed preferences for integrated, practice-oriented education addressing AI use, occupational therapy clinical reasoning, client-appropriate tool selection, assessment, and intervention planning.
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BibTeX
@article{park2026selfreported,
title = {{Self-Reported AI Competency and Educational Needs for Integrated AI and Digital Rehabilitation Education Among Occupational Therapy Students: A Cross-Sectional Survey.}},
author = {Yun-Hee Park and Deok-Ju Kim},
journal = {Journal of multidisciplinary healthcare},
year = {2026},
month = nov,
doi = {10.2147/jmdh.s650490},
url = {https://europepmc.org/article/MED/42999856},
note = {CC BY-NC},
}
RIS
TY - JOUR
TI - Self-Reported AI Competency and Educational Needs for Integrated AI and Digital Rehabilitation Education Among Occupational Therapy Students: A Cross-Sectional Survey.
AU - Yun-Hee Park
AU - Deok-Ju Kim
PY - 2026
DA - 2026/11/03
JO - Journal of multidisciplinary healthcare
DO - 10.2147/jmdh.s650490
UR - https://europepmc.org/article/MED/42999856
AB - The growing use of generative artificial intelligence (AI) in healthcare calls for occupational therapy education that combines critical appraisal, clinical reasoning, and digital rehabilitation. This study aimed to examine students' interest in AI, perceived AI competency, and needs for integrated AI and digital rehabilitation education, and identified factors associated with educational content needs. An exploratory cross-sectional survey using convenience sampling and five researcher-developed scales without full psychometric validation was conducted among occupational therapy students from three universities. A total of 250 questionnaires were submitted, and 230 complete responses were analyzed. Descriptive statistics, reliability tests, Welch group comparisons, Pearson correlations, and hierarchical multiple regression with sensitivity analyses were used. Mean scores were 3.64±0.77 for AI interest, 3.42±0.67 for self-reported AI competency, 3.93±0.68 for educational content needs, 3.95±0.63 for educational priority, and 3.82±0.64 for instructional method preference. All five domains were positively correlated. Educational priority and instructional method preference showed the strongest correlation (r=0.701). AI interest (β=0.490, p<0.001) and self-reported AI competency (β=0.154, p=0.015) were associated with educational content needs in the conventional regression; the final model explained 34.3% of the variance. The competency association was sensitive to robust inference (HC3 p=0.0504). Students expressed preferences for integrated, practice-oriented education addressing AI use, occupational therapy clinical reasoning, client-appropriate tool selection, assessment, and intervention planning.
SN - 1178-2390
ER -