Abstract:

Artificial Intelligence (AI) is increasingly integrated into higher education for teaching, learning, and assessment. However, faculty adoption of AI technologies remains uneven, shaped by cultural, ethical, and identity-related concerns unique to universities. Existing theories such as the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT) have provided powerful insights into technology adoption. Yet, they insufficiently capture socio-cultural dynamics like academic freedom, perceived threat to expertise, and institutional governance. This paper proposes an extended TAM/UTAUT framework to explain faculty adoption of AI tools in higher education. Drawing from IT identity theory (Carter et al., 2020; Vaast & Pinsonneault, 2021) and recent studies on academic freedom and responsible AI adoption (Joudieh  et al., 2024; Dotan et al., 2024; Jin et al., 2025), we conceptualize new moderating constructs and policy-level influences. A mixed-method design is proposed: qualitative interviews refine constructs, followed by a large-scale survey tested through structural equation modeling (SEM). Illustrative findings show that academic freedom positively moderates perceived usefulness, while perceived threat to expertise negatively moderates behavioral intention to adopt AI. Ethical concerns act as a mediating factor, linking trust and institutional support to adoption. The study extends acceptance theory in the context of AI, providing both theoretical advancements and practical implications for institutional AI adoption strategies.