ADOPTION OF AI-POWERED LEARNING TECHNOLOGIES AMONG MEDICAL, NURSING, AND ALLIED HEALTH FACULTY MEMBERS: THE MEDIATING ROLE OF KNOWLEDGE USING THE TECHNOLOGY ACCEPTANCE MODEL
TL;DR
The findings suggest that educational institutions should focus on improving the quality of AI-related learning resources and faculty development initiatives to strengthen AI competence and encourage the effective integration of AI-powered learning technologies into health professions education.
Abstract
Artificial Intelligence (AI) has emerged as a transformative technology in higher education, particularly in health professions education, by enhancing teaching effectiveness, personalized learning, assessment, and academic decision-making. Despite its growing significance, the successful adoption of AI-powered learning technologies among faculty members remains influenced by several technological and cognitive factors. Drawing on the Technology Acceptance Model (TAM), this study investigates the influence of content quality on faculty members' knowledge of AI-powered learning technologies and their intention to use these technologies in medical, nursing, and allied health education. Furthermore, the study examines the mediating role of knowledge in the relationship between content quality and intention to use AI-powered learning technologies. A quantitative research design was adopted using a structured questionnaire administered to 260 faculty members from medical, nursing, and allied health institutions through convenience sampling. Structural Equation Modeling (SEM) was employed to examine the proposed relationships among the constructs. The findings reveal that content quality has a significant positive effect on knowledge (β = .700, p < .001) and intention to use AI-powered learning technologies (β = .382, p < .001). Knowledge also demonstrates a significant positive influence on intention to use (β = .220, p = .034), indicating partial mediation. The measurement model confirms satisfactory construct validity, while the structural model exhibits acceptable fit indices (NFI = .877, IFI = .888, CFI = .888). The study contributes to the Technology Acceptance Model by highlighting knowledge as an important mediating construct that links high-quality AI learning content with faculty adoption intentions. The findings suggest that educational institutions should focus on improving the quality of AI-related learning resources and faculty development initiatives to strengthen AI competence and encourage the effective integration of AI-powered learning technologies into health professions education.