Faculty perceptions and implementation strategies for AI personalized learning systems at Hail University

Abstract Faculty adoption of AI-powered personalized learning systems remains inconsistent despite documented pedagogical benefits. This study investigated faculty perceptions and adoption intentions using an integrated theoretical model synthesizing Technology Acceptance Model, Constructivist Learning Theory, and Technological Pedagogical Content Knowledge frameworks. A cross-sectional survey of 134 faculty members at Hail University, Saudi Arabia, a context of large-scale AI implementation under Vision 2030, examined four perception dimensions: Teacher Effectiveness, Educational Access Equity, Quality of Learning Outcomes, and Lifelong Learning Opportunities. Using Partial Least Squares Structural Equation Modeling, results revealed that Teacher Effectiveness (β = 0.384, p < 0.001) and Lifelong Learning Opportunities (β = 0.481, p < 0.001) significantly predicted adoption intentions, with the model explaining 87% of variance. Notably, professional development opportunities emerged as the strongest predictor, challenging assumptions that adoption depends primarily on instrumental usefulness. Educational Access Equity and Quality of Learning Outcomes influenced adoption through mediated pathways, demonstrating that faculty conceptualize adoption as interconnected pedagogical considerations rather than independent factors. Findings demonstrate that educational technology adoption requires frameworks extending beyond organizational models to incorporate pedagogical values and professional development dimensions. Institutional leaders must position AI adoption around complementary value propositions emphasizing teaching effectiveness, equitable outcomes, and educator growth rather than technical efficiency alone.