The increasing use of generative artificial intelligence (AI) in higher education requires a deeper understanding of the factors that influence its adoption. While traditional technology acceptance models have been widely used to study technology adoption, their ability to explain AI adoption in academic settings remains unclear. This study conducts a comparative analysis of major technology acceptance theories, identifying historical development and key variables and highlighting their differences. Using quantitative techniques such as factor analysis, this research examines how frequently different acceptance factors appear in these models and how they relate. Findings reveal that while core factors like perceived usefulness, ease of use, and social influence remain important, AI adoption also involves unique challenges, such as trust in AI, transparency, ethical concerns, and adaptability. This research proposes an adapted AI acceptance framework for higher education to address this gap, integrating AI-specific factors with established acceptance constructs. By refining existing models to better reflect AI adoption, this study contributes to both theoretical understanding and practical implementation strategies for AI in education. The proposed framework provides higher education institutions, policymakers, and researchers with a structured approach to support AI adoption, ensuring both effectiveness and ethical responsibility in academic environments.

Keywords: Artificial Intelligence, AI Acceptance, Digital Learning, Higher Education, Technology Adoption, Technology Integration.