ARTIFICIAL INTELLIGENCE AND UNIVERSITY GOVERNANCE: A PLS-SEM MODEL OF TECHNOLOGICAL ADOPTION, ACADEMIC QUALITY, AND INSTITUTIONAL TRUST

Abstract

The integration of artificial intelligence into higher education institutions has generated new opportunities for improving governance processes, decision-making systems, and academic management. Universities increasingly rely on intelligent technologies to process large volumes of institutional data, support policy development, and enhance educational quality. However, the relationship between artificial intelligence adoption and governance outcomes remains an emerging area of empirical research.

The objective of this study is to analyze the structural relationships between artificial intelligence adoption and university governance outcomes through a Structural Equation Modeling approach estimated using Partial Least Squares (PLS-SEM). The proposed model examines the causal relationships among six latent constructs: artificial intelligence adoption, perceived technological benefits, technological challenges, policy development, academic quality, and institutional trust. Data were collected through a structured questionnaire using reflective indicators measured on a seven-point Likert scale.

The results indicate that artificial intelligence adoption significantly influences perceived technological benefits, which in turn affect policy development and academic quality. Policy development also contributes positively to academic quality, highlighting the importance of governance frameworks that integrate technological innovation into institutional management. Furthermore, both academic quality and technological challenges influence institutional trust, suggesting that stakeholder confidence depends on the effective and responsible implementation of intelligent systems within university governance structures.

The structural model demonstrates satisfactory explanatory power and acceptable model fit indicators, confirming the relevance of artificial intelligence as a driver of institutional transformation in higher education. The findings suggest that universities that strategically integrate artificial intelligence technologies into governance processes are better positioned to enhance academic performance, improve administrative efficiency, and strengthen stakeholder trust.

This study contributes to the literature on digital transformation and higher education governance by proposing an empirical model that explains how artificial intelligence adoption influences institutional outcomes through governance mechanisms and academic management processes.