Examining the Impact of Artificial Intelligence Implementation on Enhancing Research Productivity in Higher Education

Abstract

This study examines the impact of Artificial Intelligence (AI) adoption on research productivity in higher education, focusing on the roles of Information Quality, System Quality, Service Quality, System Usage, and User Satisfaction. A quantitative approach was employed, with data collected from 120 respondents via a structured Google Forms survey and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) through SmartPLS. The findings show that User Satisfaction is the strongest predictor of research productivity, highlighting the importance of user-centered design and functionality in AI tools. Other constructs, such as Information Quality and System Usage, had weaker direct effects, indicating their influence may be mediated by satisfaction. The study contributes a validated framework for measuring AI effectiveness in academic settings and offers both theoretical insights and practical guidance for higher education institutions and AI developers. Future research should include longitudinal designs, cross-disciplinary comparisons, and the integration of emerging AI technologies to broaden the framework's relevance. Overall, the study underscores AI's transformative potential in academia while identifying key factors necessary for optimizing its adoption to enhance research excellence.

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