A Study of Artificial Intelligence (AI) Use Among Entry-Level Occupational Therapy Doctoral Students.
Artificial intelligence (AI) is increasingly integrated into higher education, yet little research explores its use among entry-level occupational therapy doctoral (OTD) students. This study developed a scale to assess AI usage and perceptions, examining differences across academic years. Eighty OTD...
| Publicado en: | Open Journal of Occupational Therapy (OJOT) Vol. 14; no. 2; pp. 1 - 16 |
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| Autores principales: | , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Open Journal of Occupational Therapy
Spring2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=194037738&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 194037738 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 21686408 HDL1 jtl: Open Journal of Occupational Therapy (OJOT) issn: 21686408 maglogo: N pubinfo: dt: Spring2026 vid: 14 iid: 2 pid: 94241 pub: Open Journal of Occupational Therapy place: Kalamazoo, Michigan artinfo: ui: 194037738 194037738 194037738 10.15453/2168-6408.2436 194037738 ppf: 1 ppct: 15 formats: fmt: @attributes: type: P tig: atl: A Study of Artificial Intelligence (AI) Use Among Entry-Level Occupational Therapy Doctoral Students. aug: au: Reinoso, Gustavo Allen-McHugh, Rachel Decker, Thomas Griffiths, Yolanda Huffman-Main, Stacey affil: Drake University - Argentina, USA sug: subj: Artificial Intelligence Utilization Students, Occupational Therapy Education, Doctoral Education, Occupational Therapy Student Attitudes Evaluation Psychometrics Human Male Female Adult Colleges and Universities Urban Areas Cross Sectional Studies Delphi Technique Convenience Sample Factor Analysis Validation Studies Multivariate Analysis of Variance Univariate Statistics Internal Consistency Analysis of Variance Post Hoc Analysis Confidence Intervals Reproducibility of Results Data Analysis Software Descriptive Statistics Adult: 19-44 years Male Female ab: Artificial intelligence (AI) is increasingly integrated into higher education, yet little research explores its use among entry-level occupational therapy doctoral (OTD) students. This study developed a scale to assess AI usage and perceptions, examining differences across academic years. Eighty OTD students from a mid-sized urban university completed a 44-item survey refined using a Delphi method with nine faculty experts. Exploratory factor analysis identified three subscales--Efficiency and Adaptability, Academic Integrity Concerns, and the Student-Professor Divide--accounting for 46.04% of the variance (Cronbach's a = .72-.93). Multivariate analysis of variance revealed significant effects of academic year on subscale scores (Wilks' Λ = .822, F (6, 150) = 2.582, p = .021, partial η² = .094). Second-year students scored highest on Efficiency and Adaptability, while first- and third-year students reported a greater Student-Professor Divide. Academic Integrity Concerns remained consistently low across groups. Students perceived professors as lacking knowledge about AI, which may not fully align with reality. These findings highlight the need for increased faculty-student dialogue to bridge perceived gaps, enhance collaboration, and support ethical AI integration in occupational therapy education. Tailored curriculum adjustments could ensure students critically engage with AI while balancing innovation and professional judgment. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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