Factors influencing the adoption of generative AI tools among university students: A structured literature review

Abstract

Generative Artificial Intelligence (GenAI) tools such as ChatGPT are changing how university students access information, write, solve problems, write code and receive personalised academic support. Their rapid uptake has also raised concerns about accuracy, trust, privacy, academic integrity and overdependence. This paper presents a structured literature review of 20 scholarly publications from 2023 to 2026 that examine what influences students’ adoption of GenAI tools. The review draws on empirical, systematic and meta analytic studies, mainly those based on the Technology Acceptance Model (TAM), the Unified Theory of Acceptance and Use of Technology (UTAUT) and UTAUT2. A thematic synthesis groups the determinants of adoption into four dimensions: technological, individual, social and contextual, and trust and risk. Perceived usefulness and performance expectancy are the most consistent positive predictors. Self efficacy, habit, facilitating conditions, social influence and academic value also support adoption. Trust, anxiety, perceived risk and AI literacy show effects that depend on context. The review argues that conventional acceptance models do not fully capture the features of GenAI, including output reliability, ethical use, academic integrity and the need for critical evaluation. It therefore proposes a multidimensional view that moves from simple acceptance toward effective and responsible adoption. The findings suggest that universities should pair access to AI tools with AI literacy training, clear institutional policies and faculty guidance. Future research should develop AI specific extensions of existing models, use longitudinal and cross cultural designs, and link GenAI use to measurable learning outcomes.