Artificial Intelligence Adoption and Quality Assurance in Higher Education: Opportunities, Challenges, and Implications for Institutional Effectiveness

Abstract

The unprecedented spread of AI technologies throughout the activities of HEIs, including teaching, assessment, administration, and student services, has altered the landscape of their operations and academic practice. Although AI holds out prospects of great personalization, increased efficiency, and evidence-based decisions, its uncontrolled use creates immediate threats to such values as academic integrity, algorithmic bias, and established standards of quality assurance (QA). This paper explores the connection between AI implementation and quality assurance in higher education, suggesting an integrated conceptual model where the variables of institutional readiness, faculty digital literacy, AI tool capability, and policy support are seen as antecedents of AI adoption with QA models moderating institutional effectiveness outcomes. The findings of a mixed-method survey among 60 HEIs and 240 faculty/administrative respondents reveal the existence of a statistically significant positive link between AI adoption and institutional effectiveness (R² = 0.61); the effect increases considerably if there is an adequate integration of QA models (effect size of integration equals 23.8%). In such cases, stakeholder confidence in transparency, fairness, and academic integrity increased by an average of 32%. Five key research gaps are highlighted, and policy implications and future research directions are offered.