Examining the determinants of AI dependency on generative artificial intelligence (GenAI) among undergraduates in a Malaysian private university

Teo, Xue Lin and Wong, Xian Xin (2026) Examining the determinants of AI dependency on generative artificial intelligence (GenAI) among undergraduates in a Malaysian private university. Final Year Project, UTAR.

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Abstract

This study explores the determinants of AI dependency on generative artificial intelligence (GenAI) among undergraduate students in a Malaysian private university. With the growing integration of GenAI tools in higher education, concerns have emerged regarding their influence on students’ learning behaviours, critical thinking abilities, and level of academic engagement. Based on the Unified Theory of Acceptance and Use of Technology (UTAUT) and Social Cognitive Theory (SCT), The study focuses on six independent variables which are academic self-efficacy, academic stress, performance expectancy, academic workload, time pressure, and social influence, and examines their effects on AI dependency. The study focuses on a private university which is UTAR in both campuses which are Kampar and Sungai Long. In this research, there are 388 respondents collected for questionnaires at both campuses successfully. The data analysed using descriptive statistics, reliability and normality tests, as well as Pearson Correlation Coefficient Analysis and Multiple Regression Analysis via Statistical Package for the Social Sciences (SPSS) version 31. The results show that academic self-efficacy is significant negative relationship with AI dependency, while academic stress is non-significant direct effect to AI dependency. Performance expectancy, academic workload, time pressure, and social influence are significant positive relationship with AI dependency. Among these variables, time pressure was identified as the strongest predictors, indicating that students tend to rely more heavily on GenAI when facing tight deadlines and when they perceive the technology as effective in improving academic performance. These findings demonstrate that both psychological factors and situational pressures play a significant role in shaping students’ dependency on AI tools. Lastly, the research has presented a summary of major findings, implications of the study, limitations and recommendations.

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