Artificial Intelligence Adoption in Higher Education: A Mediation-Based Conceptual Framework for Teaching Innovation and Learning Effectiveness Yousef Mohammad Iriqat
Al-Quds Open University
Technology and Applied Science Department
Jericho Branch, Jericho, Palestine
Abstract
Artificial Intelligence (AI) is rapidly transforming higher education by enabling innovative teaching practices and enhancing 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 improved learning effectiveness. This study addresses this gap by proposing an integrated conceptual framework that explains how technological and institutional factors influence AI adoption and how AI adoption subsequently enhances teaching innovation and learning outcomes.
Exploring established theoretical perspectives, 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 drive AI adoption, while teaching innovation mediates the relationship between AI adoption and learning effectiveness. Methodologically, the study proposes the use of Partial Least Squares Structural Equation Modeling (PLS-SEM) to evaluate the direct and mediating relationships among the constructs.
The proposed framework suggests that AI infrastructure and institutional support facilitate AI adoption in higher education institutions. AI adoption, in turn, enables innovative teaching practices that enhance learning effectiveness. By integrating technological, organizational, and pedagogical perspectives, this study contributes to the literature by extending technology adoption models and providing a comprehensive framework for understanding the educational value of AI in higher education.
