Factors Affecting University Students Intention to Use Generative Artificial Intelligence: Integrating Technology Acceptance Model

Abstract

The rapid rise of Generative Artificial Intelligence (GenAI) technologies such as ChatGPT, Sora, DeepSeek, and Google Gemini is transforming the landscape of higher education by enabling new forms of learning, academic support, and content creation. Despite this technological momentum, limited empirical research exists on the determinants influencing students’ behavioral intention to adopt GenAI, especially in developing countries like Bangladesh. This study aims to address this gap by examining university students’ intention to use GenAI tools through an extended Technology Acceptance Model (TAM) that incorporates AI Literacy (AIL) and Learning Value (LV), in addition to the classical constructs of Perceived Usefulness (PU) and Perceived Ease of Use (PEU). The study also evaluates the mediating role of Attitude (ATT) in shaping students’ adoption behavior. A quantitative research design was employed, and data were collected via an online survey from 230 university students in Bangladesh. The analysis was conducted using SmartPLS, a Partial Least Squares Structural Equation Modeling (PLS-SEM) tool, to assess the direct, indirect, and total effects among constructs. The findings confirm that Perceived Usefulness significantly influences both Attitude and Intention to Use, whereas Perceived Ease of Use has a significant direct effect only on intention. Learning Value positively affects attitude, but not behavioral intention directly. Surprisingly, AI Literacy exhibited no significant impact on either attitude or intention. Additionally, Attitude was found to significantly mediate the effects of PU and LV on intention, reaffirming its crucial role in technology acceptance. The study contributes to theoretical understanding by validating an expanded TAM framework that incorporates modern adoption variables relevant to GenAI contexts. It also offers practical insights for educators, policymakers, and EdTech developers on how to enhance student adoption by emphasizing functional value, educational benefits, and attitudinal support. Although limited by scope and geographic focus, the findings provide a foundation for future research in diverse educational and technological settings.