Factors Influencing University Students’ Intention to Use and Reliance on Generative Artificial Intelligence: An Extended Technology Acceptance Model with Critical Use

In light of the growing popularity, accessibility, and utility of Generative Artificial Intelligence (GenAI) tools, their impact on learning can be either supportive or detrimental, depending on how students use them. This calls for an examination of students’ intention to use and reliance on GenAI to inform timely interventions within higher education to ensure favourable learning outcomes. This study investigates factors influencing intentions to use and reliance on GenAI among Engineering and Computer Science/ Information Technology (CS/IT) students. Drawing on an extended Technology Acceptance Model (TAM), the study integrates the construct of critical use and conceptualises reliance across four functional domains relevant to engineering and CS/IT education. Data from an anonymous survey collected at an Australian university (n = 126) are analysed using the Partial Least Squares Structural Equation Modelling (PLS-SEM) approach. The modelling results show that attitudes towards GenAI and critical use directly and positively influence intention to use GenAI, while perceived ease of use and perceived usefulness have positive indirect effects. The intention to use, in turn, significantly predicts reliance on GenAI across the four domains: understanding, assessment, programming, and engineering projects. Students demonstrate a moderate level of reliance overall, with greater use for understanding-related tasks and limited utilisation for full assessment writing. This study offers insights for higher education institutions aiming to foster ethical and critical use of GenAI for learning.