Beyond the Hype: Generative Artificial Intelligence Adoption and Academic Performance among Higher Education Students

Citation

Yap, Yee Yann and Tan, Siow Hooi and Tan, Booi Chen (2025) Beyond the Hype: Generative Artificial Intelligence Adoption and Academic Performance among Higher Education Students. In: 2025 17th International Conference on Education Technology and Computers, ICETC 2025, 18 September 2025 - 21 September 2025, Barcelona.

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Abstract

Generative artificial intelligence (GenAI) tools like ChatGPT are increasingly integrated into higher education due to their capabilities in content generation, academic support, and personalized learning. However, questions remain regarding the factors that drive or hinder their adoption and the extent to which they influence students’ academic performance. This study addresses these gaps by extending the Technology Acceptance Model (TAM) with barrier-related constructs to examine GenAI adoption among Malaysian university students. Data were collected through cross-sectional questionnaires survey from 177 Malaysian university students. Partial least squares structural equation modeling was used to analyze the data. The findings revealed that the perceived usefulness drove GenAI adoption, while deceptiveness and information overload hindered it. Perceived ease of use did not have any significant impact on adoption. On the other hand, the results indicated that the integration of GenAI was positively related to students’ academic performance. These results enhance further understanding of GenAI adoption in higher education and have a useful implication on stakeholders willing to exploit the full potential of AI technologies in learning and teaching.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Artificial intelligence, generative AI
Subjects: Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions: Faculty of Management (FOM)
Depositing User: Ms Rosnani Abd Wahab
Date Deposited: 20 Apr 2026 03:51
Last Modified: 20 Apr 2026 03:56
URII: http://shdl.mmu.edu.my/id/eprint/15778

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