Automating Quality Assurance: Artificial Intelligence Applications In Accreditation And Performance Monitoring In Higher Education

Abstract

An automated, data-driven, and continuous evaluation process is increasingly being the reality of quality assurance in higher education, largely thanks to the advent of artificial intelligence (AI). This review explores the use of AI for accreditation, compliance checking, and monitoring institutional performance. Literature is reviewed and consolidated on the role of machine learning, natural language processing, predictive analytics and intelligent decision-support systems for evidence automation, performance indicator monitoring, as well as academic risk prediction and accreditation decisions. The review reveals that AI can help streamline administration, boost monitoring efficiency, identify performance gaps early on, and aid in ongoing quality improvement. Yet, issues of data quality, privacy, algorithmic bias, explainability, interoperability and institutional readiness are still prevalent. The study underscores the importance of developing AI-driven, transparent, and responsible frameworks in higher education and emphasizes potential future research avenues for sustainable and trustworthy AI-based quality assurance systems in higher education.