A Study of Artificial Intelligence (AI) Use Among Entry-Level Occupational Therapy Doctoral Students

Abstract

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 α = .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.

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