Artificial Intelligence in Higher Education Governance: Integrating AI into Quality Assurance and Strategic Management

Artificial intelligence (AI) is rapidly transforming higher education, yet research has largely focused on pedagogical applications while its role in governance and quality assurance (QA) remains insufficiently synthesized. This study examines how AI is integrated into higher education governance and QA systems and evaluates its institutional implications. Using the PRISMA 2020 protocol, a systematic literature review was conducted in the Web of Science Core Collection. From an initial dataset of 177 publications, 48 studies met the inclusion criteria after multi-stage screening. The selected studies were analyzed through a SWOT-based thematic synthesis to identify the strengths, weaknesses, opportunities, and threats associated with AI adoption in governance and QA processes. The findings show that AI increasingly functions as a governance infrastructure supporting predictive analytics, continuous monitoring, and data-driven institutional decision-making. Key strengths include enhanced transparency, automation of quality processes, and improved strategic foresight. However, structural weaknesses such as fragmented data infrastructures, limited digital capacity, and insufficient regulatory frameworks remain significant barriers. The analysis also identifies risks related to algorithmic bias, governance opacity, and institutional inequality. Based on these findings, the study proposes the AI-Enabled Quality Governance (AEQG) Model, conceptualizing AI integration as a layered governance architecture linking institutional capacity, AI-driven decision intelligence, quality assurance mechanisms, and ethical oversight.