AI-powered pathways: empowering evidence-based practice skills for diverse learners in occupational therapy education
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Citation
Abstract
RESEARCH PROBLEM: How can the strategic integration of responsible artificial intelligence (AI) technology enhance evidence-based practice (EBP) education for diverse learners in entry-level occupational therapy (OT) programs while simultaneously strengthening occupation-centered professional reasoning skills? Background: Entry-level occupational therapy students (EL-OTS) face significant challenges and cognitive load when learning multifaceted evidence-based practice (EBP) methodologies because they lack the clinical experience and contextual understanding necessary to apply flexible EBP models. Students with diverse learning needs experience additional barriers when navigating multiple procedural steps, complex search engines, and interdisciplinary EBP models that obscure OT's core tenet of occupation as a driver for health and well-being. Current literature lacks occupation-centered EBP curricula tailored to diverse learners' needs, and there is a gap in research examining how AI tools can support diverse learning needs in occupational therapy education.
PURPOSE: This doctoral project develops a program that integrates responsible AI within innovative evidence-based practice (EBP) learning modules to supplement an entry-level occupational therapy program's core curriculum, while designing and evaluating the impact of accessible, occupation-centered EBP learning experiences for diverse entry-level occupational therapy students (EL-OTS).
METHODS: Using a mixed-methods, single-subject study design with a diverse EL-OTS volunteer, this project will implement asynchronous, interactive learning modules informed by the Knowledge to Action (KTA) framework, the Subject-Centered Integrative Learning Model for Occupational Therapy (SCIL-OT), and other evidence-based strategies for accessible curricula in higher education. Quantitative data will be collected through standardized pre- and post-intervention assessments using the Evidence-Based Practice Confidence (EPIC) Scale and Adapted Fresno Test (AFT), while qualitative data will be gathered through open-ended surveys and reflective journals. Data analysis includes descriptive statistics, paired t-tests, and thematic coding.
RESULTS: Anticipated results will show that EL-OTS demonstrates improved EBP competence, confidence, and ability to integrate occupation-centered reasoning skills for real-world applications. The integration of responsible AI within occupation-centered, accessible EBP education will address persistent barriers that diverse EL-OTS face when applying evidence to practice. This study suggests that responsible AI integration within occupation-centered, accessible EBP education offers a promising pathway to address persistent barriers faced by diverse EL-OTS. OT educators will utilize findings to inform broader curriculum development by implementing innovative, accessible teaching approaches that bridge the research-to-practice gap in alignment with the Knowledge to Action framework. The AI-powered Pathways program will enable diverse EL-OTS to deliver evidence-informed, occupation-centered care in an increasingly technology-driven healthcare landscape while upholding the profession's commitment to occupational justice and inclusive higher education.
Plain Language Summary
This doctoral project aims to help diverse students in entry-level occupational therapy programs learn how to use evidence-based practice skills more effectively. The AI-powered Pathways program tailors artificial intelligence responsibly in innovative learning modules to help occupational therapy students develop effective evidence-based practice skills. By making learning more accessible while maintaining focus on meaningful occupations, these modules address challenges faced by diverse learners struggling with complex research practices.
The study will use established tests and survey questions to measure how students' evidence-based practice knowledge, application, and confidence improve when using these AI-enhanced modules. Findings will provide insights for creating more accessible teaching approaches that prepare occupational therapy students to deliver evidence-informed, occupation-centered care for all people. This research could help shape future occupational therapy education, making it more inclusive and better preparing diverse students for today’s technology-driven healthcare environment.
Description
2025
License
Attribution-NonCommercial-NoDerivatives 4.0 International