Enhancing quality assurance through strategic artificial intelligence integration: a framework for higher education digital transformation

Purpose This paper aims to present a comprehensive quality assurance framework for higher education institutions integrating artificial intelligence (AI). Rooted in digital transformation theory, this exploration examines how generative AI can enhance quality assurance and address implementation challenges. The study uses systematic content analysis to identify evidence-based methods for leveraging AI to improve institutional effectiveness and educational quality. Design/methodology/approach This research addresses a gap in quality assurance frameworks for integrating AI in higher education. Existing literature often focuses on AI risks but neglects systematic approaches for enhancing quality assurance through the use of AI. This study offers leaders practical frameworks for leveraging AI to enhance quality assurance and educational integrity. Findings Strategic AI integration significantly boosts quality assurance through automated data analysis, pattern detection and predictive analytics. AI has the potential to strengthen analytics, enable proactive quality management and automate evidence collection. Successful implementation requires risk management, stakeholder-centric policy development and training. Research limitations/implications Higher education institutions can use this framework to develop AI-enhanced quality assurance systems, significantly improving the efficiency, accuracy and scope of institutional evaluations. The implementation model offers actionable guidance for strategic AI adoption while maintaining educational quality and institutional integrity. This framework helps institutions transition to proactive, data-driven quality management systems. Originality/value This study presents the first comprehensive framework specifically designed to enhance quality assurance through the integration of AI in higher education. It addresses quality improvement opportunities and risk management, offering institutional leaders a balanced methodology for AI-enhanced quality assurance.