Rethinking Generative AI Continuance in Higher Education: The Mediated Role of Learning Value

Abstract

In higher education, learning value increasingly drives students’ continuous engagement with Generative AI tools such as ChatGPT. By re-conceptualizing the UTAUT framework, this study examines university students’ post-adoption and continuance usage intention towards ChatGPT. The study positioned learning value as a mediating variable linking performance expectancy, effort expectancy, perceived trust and hedonic motivation with continuance usage intention. With data collected from 351 university students, this study combines PLS-SEM and ANN analysis to understand both linear and nonlinear relationships among the variables. The findings establish that learning value is the strongest driver of continuance usage intention and significantly mediates the effect of performance expectancy, effort expectancy and hedonic motivation while perceived trust has a significant direct influence on continuance intention, with no mediating effect with learning value. Effort expectancy, on the other hand, does not have a significant influence on continuance intention, suggesting that ease of use has become a hygiene factor in the post-adoption learning context. The results highlight the importance of re-conceptualizing the UTAUT model towards a learning-centered approach where educational value, an interactive user interface and trustworthiness drive continuous usage intention of Generative AI tools. The findings highlight the importance of integrating Gen-AI tools into pedagogical designs that promote critical thinking rather than using them for mere task completion. This paper suggests longitudinal and cross-country comparisons examining the ethical and responsible use of ChatGPT in the learning process of university students as the scope for future research.