Exploring perceptions of school-based OT practitioners on implementing AI to optimize workload

Becerra, Noemi and Bernardo, Darryl and Nguyen, Ngoc and Perez, Kevin (2025) Exploring perceptions of school-based OT practitioners on implementing AI to optimize workload. Masters thesis, Stanbridge University.

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Abstract

School-based occupational therapy practitioners (SBOTPs) face increasing workloads that contribute to burnout and reduce time for direct intervention. Artificial intelligence (AI) has emerged as a potential tool to optimize workload by automating documentation, research, and intervention planning. However, successful implementation requires understanding SBOTPs’ perceptions, concerns, and readiness for AI integration. This study explores SBOTPs’ perspectives on AI’s role in optimizing workload through an online survey. Participants (N = 231) shared insights into their current workload demands, estimated time savings with AI, and specific tasks AI could support. Findings indicate that 63% of respondents believe AI can optimize workload, with newer and highly experienced practitioners being the most receptive, only 2% outright stating that AI cannot optimize workload, and the remainder expressing uncertainty. Documentation (84%) and research (79%) were the most commonly cited tasks in which AI can support. Key concerns included reliability (72%), data privacy (69%), and lack of training (64%). While some SBOTPs actively use AI tools like ChatGPT and Magic School AI, others remain hesitant due to institutional barriers or lack of familiarity. These findings highlight the need for targeted AI training, addressing ethical guidelines, and institutional support to facilitate effective AI integration for SBOTPs. This study contributes to ongoing discussions on AI in occupational therapy and highlights opportunities to improve practitioner efficiency while maintaining high-quality services.

Item Type: Thesis (Masters)
Uncontrolled Keywords: MSOTLA002
Subjects: R Medicine > R Medicine (General)
Depositing User: Kareena Yashko
Date Deposited: 25 Mar 2026 20:29
Last Modified: 25 Mar 2026 20:29
URI: http://repository.stanbridge.edu/id/eprint/215

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