Bridging innovation and resistance: A theoretical framework for understanding enablers and barriers of AI-Driven E-Learning in higher education

Artificial intelligence integration into higher education has revolutionized e-learning, fostering adaptive, scalable, and personalized learning experiences. Yet, extensive AI adoption is marred by recurring technology, institution, pedagogy, and ethics challenges resulting in uneven adaptation. Employing a systematic literature review approach, the paper aggregates studies between 2020 and 2025, contextualizing the synthesis from six keyword datasets pulled from the Scopus database. The research outlines enablers like access, organizational support, and behavioral intention, and barriers like poor infrastructure, low training, and low digital literacy. The conceptual AI-ELAM framework leverages the Unified Theory of Acceptance and Use of Technology (UTAUT), the Technology Organization–Environment (TOE) framework, and the Diffusion of Innovation (DOI) theory, integrating them into a cohesive model for AI-driven e-learning adoption. This model emphasizes the necessity of government policy as enablers in bridging theoretical readiness to tangible uptake by crossing system barriers. The study indicates that catalytic drivers are not enough; there should be policy intervention in addressing infrastructural and skill-based barriers. Government intervention through digital policy, infrastructure investment, and training capabilities is crucial to realizing equal and sustainable AI uptake. The study makes theoretical contributions by building on the existing technology adoption models, and offers actionable advice to policymakers and institution leaders. It follows that there has to be a coordinated, systemic process being one of aligning facilitators, identifying barriers, and using policy as a facilitator to ensure successful AI-facilitated e-learning introduction in higher education.