Nonparametric efficiency and artificial intelligence techniques in higher education: a systematic literature review and bibliometric analysis

Abstract

In a sector that needs to work as efficient as possible, artificial intelligence (AI) can guide the efficiency improvements of higher education institutions (HEIs). This paper explores both the AI literature and the efficiency literature as applied to HEIs following the Preferred Reporting Items for Systematic Review guidelines. The goal is to identify the relevant research that uses nonparametric efficiency and AI techniques within the HEI sector by examining articles published up to March 2025. Our findings provide a powerful mix of bibliometric and systematic literature review results that identify the main trends common to these two strands of research. The analysis highlights a long-standing tradition of applying nonparametric efficiency analysis to the sector, as it is attracting the increasing attention of AI scholars. We outline the substantial evidence that reveals much room for improvement in efficiency in the HEI sector, and how the application of AI may be well-suited. This is particularly evident as AI can support efficiency evaluations, particularly in handling tasks that traditional efficiency techniques alone cannot perform. A key contribution of this work is the identification of the opportunities for further research focus within this critical intersection between the two fields, which can inform both HEI administrators and policymakers.