AI Acceptance for Learning in Higher Education: A Review of Human Capability, Trust, Governance, and Self-Efficacy Factors

Artificial Intelligence (AI) is changing higher education in many ways, through intelligent mentoring systems, enabling adaptive learning platforms, acquiring knowledge on analytics by learning, automated assessment, and generative AI tools. As this AI started tremendously incorporated into educational settings so it is important to know which factors effect AI Acceptance for Learning. The current study reviews and combines the literature on Artificial intelligence acceptance for educating in university education (AI Literacy, AI Experience, Responsible AI Governance, Trust in AI, and AI Self-Efficacy). We followed a Systematic Literature Review (SLR) process to recognize, filter and analyze similar studies.  In this paper the findings shows that AI Literacy and AI Experience raise the learners’ preparedness and confidence in intriguing with AI technologies at the same time Responsible AI Governance promotes transparency, accountability, fairness, and ethical implementation by establishing trust. The important predictor in AI Acceptance for leaning is trust and AI Self-Efficacy is positively impacting the behavioral intention and engagement. The review also points to the need for integrated frameworks that take account of capability, governance, trust and behavioural factors at the same time. From the observations it provides valuable understandings for researchers and Graduate education institutions interested in promoting responsible and effective adoption of Artificial intelligence in teaching and learning.