Generative AI Adoption in Higher Education: Aligning Academic Tasks, Technology, and Learning

Abstract

Generative Artificial Intelligence (GenAI) is reshaping education by transforming teaching, learning, and assessment practices, making it vital to examine its effective integration within academic contexts. This study explores the adoption of GenAI tools and their impact on academic performance among ICT students at a private Australian higher education institute. Drawing on the Theory of Planned Behavior (TPB) and Task–Technology Fit (TTF) frameworks, it investigates how behavioural intentions, pedagogical factors, and technological characteristics collectively influence students’ use of GenAI and academic performance. A positivist, quantitative design was adopted, using an anonymous self-administered questionnaire completed by 235 students across Melbourne and Sydney campuses. Data were analysed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings highlight three insights. First, Perceived Behavioural Control emerged as the strongest predictor of students’ intention to adopt GenAI, highlighting that confidence and a sense of control are critical drivers of adoption. Attitudes also positively influenced intention, while social influence was weaker, underscoring the need to strengthen students’ self-efficacy and AI literacy. Second, TTF, shaped by pedagogical task variables and technological characteristics, directly affected both use and academic performance. Extending TTF to include learning outcomes and assessment methods demonstrates that curriculum design is central to technology-task alignment. Third, TTF emerged as the strongest driver of academic performance, exceeding both behavioural intention and actual use. Meaningful gains occur when GenAI is effectively aligned with tasks. By integrating TPB and TTF, this study provides a comprehensive framework for GenAI adoption and guidance for embedding AI into higher education.