FACTORS INFLUENCING UNIVERSITY STUDENTS' ACCEPTANCE OF ARTIFICIAL INTELLIGENCE FOR LEARNING

Abstract

This narrative literature review synthesizes empirical and conceptual research on the factors that shape university students' acceptance of artificial intelligence (AI) tools, particularly generative AI applications such as ChatGPT, for learning purposes. Drawing on the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT) as the dominant theoretical lenses, the review organizes the literature into five thematic clusters: (1) cognitive-instrumental determinants, namely perceived usefulness and perceived ease of use; (2) social and normative determinants, including subjective norms and social influence; (3) affective and dispositional determinants, such as trust, perceived risk, anxiety, and hedonic motivation; (4) ethical and integrity-related determinants concerning academic honesty, data privacy, and algorithmic bias; and (5) sociodemographic and contextual determinants, including gender, digital literacy, and institutional support. The synthesis indicates that while perceived usefulness and ease of use remain robust predictors of behavioral intention across contexts, their explanatory power is increasingly moderated by trust, ethical perception, and institutional policy as generative AI becomes normalized in higher education. Gender and digital-literacy gaps persist as salient moderators of adoption behavior, and unresolved concerns about academic integrity continue to complicate students' attitudes even when instrumental benefits are recognized. The review concludes that AI acceptance in higher education is best understood as a multidimensional and context-dependent process rather than a purely technical adoption decision, and it offers implications for theory extension and institutional policy design, along with directions for future confirmatory research.