Examining the drivers of ai technologies for academic productivity among higher education students

Lee, Esther Ke Xin (2025) Examining the drivers of ai technologies for academic productivity among higher education students. Final Year Project, UTAR.

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Abstract

The study examines the drivers of artificial intelligence (AI) technologies in shaping academic productivity among higher education students. The rapid advancement of AI has transformed educational practices, making AI technologies increasingly integrated into students’ academic routines. As the role of AI expands in academic settings, understanding how students perceive and engage with these technologies is essential. While AI technologies are widely recognised for their potential to enhance learning, concerns remain about the extent to which students may become dependent on them. Grounded in the Technology Acceptance Model (TAM) and self-efficacy theory, this study explores how students' perceptions of AI technologies, particularly perceived usefulness, perceived ease of use and perceived self-efficacy, shape their academic dependence on AI technologies, and how such dependence subsequently affects their academic productivity. To empirically test these relationships, a quantitative research design was adopted. Primary data were collected through a questionnaire survey distributed to students enrolled in higher education institutions. Additionally, partial least squares structural equation modeling (PLS-SEM) was employed to analyse the relationships among the constructs. This approach enabled both the assessment of construct reliability and validity within the measurement model and the testing of hypothesised relationships in the structural model. The findings of the study are expected to contribute to the literature on the role of AI technologies in academic contexts by clarifying the interplay between perceptions, reliance, and productivity. Theoretically, the research extends TAM by incorporating self-efficacy and by emphasising academic dependence as a critical construct linking perceptions and productivity. Practically, the study provides insights for higher education institutions, educators, and policymakers seeking to integrate AI technologies effectively and sustainably. Looking ahead, the study provides a foundation for future research and policy initiatives on promoting responsible AI use while supporting students’ independent learning and long-term academic development. Keywords: Artificial Intelligence (AI) technologies, perceived usefulness, perceived ease of use, perceived self-efficacy, academic dependence, academic productivity, higher education institutions

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