From Engagement to Empowerment: AI-Powered Mobile Applications and the UTAUT Framework in Higher Education

Abstract

This mixed-methods study investigates how AI-powered mobile learning applications impact student engagement and empowerment in higher education. By utilizing the Unified Theory of Acceptance and Use of Technology (UTAUT) framework and quantitative analysis (SEM), this paper examines the influence of performance expectancy, effort expectancy, social influence, and facilitating conditions on student adoption and use of AI-powered mobile learning applications. In this study, qualitative data (thematic analysis of semi-structured interviews) explored the nuanced experiences of students regarding personalization, motivation, and self-efficacy. Results revealed strong predictive power of performance and effort expectancy in technology adoption, with AI features significantly impacting student self-efficacy and motivation. The study also highlights the potential of AI-powered mobile applications to foster student empowerment, moving beyond engagement to self-directed learning and enhanced academic success. Students reported increased engagement, confidence, and control over their learning. However, the study’s reliance on self-reported data and a single university sample limits generalizability. Future research should address these limitations through longitudinal studies and diverse participant populations to further explore the long-term impacts of AI-powered mobile learning on student success.