Case-based reasoning for intelligent quality assurance in higher education: a systematic literature review

The advancement of information technology and artificial intelligence has introduced a new paradigm in quality management for higher education. One promising approach is case-based reasoning (CBR), which enables intelligent decision-making based on prior experiences. However, there has been no systematic review explicitly mapping how CBR contributes to quality assurance in higher education. This study aims to identify, analyse, and synthesize key findings from the literature regarding the application of CBR in the context of improving higher education quality. The methodology adheres to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol, with selection criteria based on the PICOC framework (Population, Intervention, Comparison, Outcome, and Context). The review results indicate that although the application of CBR in higher education remains relatively limited, its development potential is significant particularly in performance evaluation systems, academic service personalization, and adaptive quality management. The current systematic review addresses this gap by following the PRISMA protocol and PICOC framework to synthesize the contributions of CBR to intelligent, adaptive, and sustainable quality assurance in higher education. The research offers a future agenda relevant to academics, practitioners, and policymakers in addressing the challenges of higher education in the digital era.