IJIET 2026 Vol.16(7): 1958-1966
doi: 10.18178/ijiet.2026.16.7.2657
doi: 10.18178/ijiet.2026.16.7.2657
The Efficiency Paradox: Modeling Student Engagement and AI Dependence on STEM Productivity in a Philippine State University
John Manuel Buniel and Kenny John Grustan *
Department of General Teacher Training, North Eastern Mindanao State University, Tandag, Philippines
Email: johncotaresbuniel@gmail.com (J.M.B.); grustankennyjohn@gmail.com (K.J.G.)
*Corresponding author
Email: johncotaresbuniel@gmail.com (J.M.B.); grustankennyjohn@gmail.com (K.J.G.)
*Corresponding author
Manuscript received January 5, 2026; revised February 25, 2026; accepted April 2, 2026; published July 22, 2026
Abstract—The integration of Generative Artificial Intelligence (GenAI) in Science, Technology, Engineering, and Mathematics (STEM) education has created an “Efficiency Paradox”, where students may rely on algorithms not out of incompetence, but to manage increasing academic workloads. Distinct from previous research that predominantly examines AI through the lens of academic integrity or general acceptance, this study investigates the psychological mechanisms linking student engagement to AI dependence and output productivity. This study empirically modeled the dual pathways of student engagement and AI dependence on output productivity. Employing a descriptive-predictive correlational design, data were collected from 915 undergraduate STEM students at a state university in the southern Philippines. Partial Least Squares Structural Equation Modeling (PLS-SEM) and Importance-Performance Map Analysis (IPMA) were utilized to analyze the relationships between digital literacy, perceived usefulness, self-efficacy, motivation, time management, AI dependence, and productivity. The structural model explained 75.2% of the variance in productivity (R2 = 0.752). The findings revealed that Time Management was the single strongest driver of both AI Dependence (β = 0.409) and Productivity (β = 0.605), suggesting that dependence is often a strategic adaptation for efficiency. Conversely, Intrinsic Motivation acted as a buffer, significantly reducing reliance on AI tools (β = −0.460). IPMA results highlight that while students possess high digital literacy, it has low impact on productivity compared to regulatory skills. The study concludes that higher education policy at the institutional level should shift focus from basic AI literacy to fostering self-regulation and time management to prevent pathological dependence.
Keywords—Artificial Intelligence (AI) dependence; Science, Technology, Engineering, and Mathematics (STEM) education; time management; student engagement; Partial Least Squares Structural Equation Modeling (PLS-SEM); efficiency paradox
Copyright © 2026 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
Keywords—Artificial Intelligence (AI) dependence; Science, Technology, Engineering, and Mathematics (STEM) education; time management; student engagement; Partial Least Squares Structural Equation Modeling (PLS-SEM); efficiency paradox
Cite: John Manuel Buniel and Kenny John Grustan, "The Efficiency Paradox: Modeling Student Engagement and AI Dependence on STEM Productivity in a Philippine State University," International Journal of Information and Education Technology, vol. 16, no. 7, pp. 1958-1966, 2026.
Copyright © 2026 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).