From Infrastructure to Interaction: How University Digital Transformation Empower AI Collaboration Quality among Malaysian University Students

Abstract

As artificial intelligence (AI) becomes increasingly embedded in higher education, universities are under growing pressure to develop institutional conditions that enable students to engage with AI tools in effective and meaningful ways. Drawing on the Job Demands–Resources (JD-R) model, this study examines how university digital transformation relates to students’ work engagement, technological self-efficacy, and student–AI collaboration quality in the Malaysian higher education context. Using survey data collected from 135 Malaysian university students, the study applies regression analysis to test the proposed relationships. The findings indicate that university digital transformation is positively associated with students’ work engagement and technological self-efficacy. Technological self-efficacy, in turn, contributes significantly to student–AI collaboration quality. These findings suggest that digital transformation functions as an important institutional resource that supports students’ motivational and cognitive readiness for technology-enabled learning. The study extends the application of the JD-R model to AI-supported higher education and offers practical implications for university management seeking to strengthen students’ technological confidence and capacity for productive AI collaboration