GENERATIVE ARTIFICIAL INTELLIGENCE IN HIGHER EDUCATION: A CONCEPTUAL FRAMEWORK LINKING UTAUT, SELF-EFFICACY, AI BEHAVIOURAL USE, AND ACADEMIC PERFORMANCE
DOI:
https://doi.org/10.35631/JISTM.1144055Keywords:
Generative Artificial Intelligence, UTAUT, Self-Efficacy, AI Behavioural Use, Academic Performance, Public Higher Education, SabahAbstract
The widespread use of Generative Artificial Intelligence (GenAI)-based tools like ChatGPT, Copilot, and Gemini has revolutionised the higher education system in Malaysia and abroad. Although there have been many examples of such approaches employed to support the teaching and learning experience, little attention is paid to how these adoption patterns influence the acceptance behaviours and internal psychological variables of students and their academic impact. In this paper, we develop a theoretical framework that integrates the Unified Theory of Acceptance and Use of Technology (UTAUT) and Self-Efficacy Theory to investigate what factors drive AI Behavioural Use and how they can mediate the relationship between AI Behavioural Use and Academic Performance. To validate this framework, a future empirical testing plan is proposed using a quantitative cross-sectional design involving 400 undergraduate students across public higher education institutions in Kota Kinabalu, Sabah, with data to be analyzed using Partial Least Squares Structural Equation Modelling (PLS-SEM) in SmartPLS 4.0. The proposed model establishes a direct pathway of Performance Expectancy, Effort Expectancy, Social Influence, Facilitating Conditions, and Self-Efficacy to AI Behavioural Use and an indirect route to Academic Performance. To this end, this study seeks to bridge the intention-behaviour gap in educational technology research, providing valuable ideas for institutional policy development, pedagogical strategy planning, and equitable distribution of digital resources within regional educational systems.
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