ARTIFICIAL INTELLIGENCE ADOPTION IN HIGHER EDUCATION: A MEDIATION-BASED CONCEPTUAL FRAMEWORK FOR LEARNING EFFECTIVENESS

Abstract

Artificial Intelligence (AI) is rapidly transforming higher education by enabling innovative teaching practices and improving student learning outcomes. Despite the growing adoption of AI technologies in educational institutions, limited research has examined the mechanisms through which AI adoption contributes to enhanced learning effectiveness. This study addresses this gap by proposing a conceptual framework that explains how technological and institutional factors drive AI adoption and how AI adoption subsequently promotes teaching innovation and learning effectiveness. Drawing on established technology adoption theories, including the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT), the study develops a mediation-based model in which AI infrastructure and institutional support influence AI adoption, while teaching innovation mediates the relationship between AI adoption and learning effectiveness. The proposed model is evaluated using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings highlight the importance of technological infrastructure, institutional support, and pedagogical innovation in enabling the successful integration of AI technologies in higher education. This study contributes to the literature by extending technology adoption models and providing a comprehensive framework for understanding how AI adoption enhances teaching practices and learning outcomes.