Artificial Intelligence and Cost Reduction in Public Higher Education: A Scoping Review of Emerging Evidence

TL;DR

This scoping review examines how AI tools like ChatGPT and predictive analytics are being used to reduce costs in public higher education, highlighting benefits, challenges, and areas needing further research.

Contribution

It provides a comprehensive synthesis of empirical evidence on AI's role in cost reduction in public higher education, identifying key applications and limitations.

Findings

AI automates administrative tasks to save costs

Predictive analytics improve student retention and planning

Implementation costs and digital divides pose challenges

Abstract

Public higher education systems face increasing financial pressures from expanding student populations, rising operational costs, and persistent demands for equitable access. Artificial Intelligence (AI), including generative tools such as ChatGPT, learning analytics, intelligent tutoring systems, and predictive models, has been proposed as a means of enhancing efficiency and reducing costs. This study conducts a scoping review of the literature on AI applications in public higher education, based on systematic searches in Scopus and IEEE Xplore that identified 241 records, of which 21 empirical studies met predefined eligibility criteria and were thematically analyzed. The findings show that AI enables cost savings by automating administrative tasks, optimizing resource allocation, supporting personalized learning at scale, and applying predictive analytics to improve student retention and institutional planning. At the same time, concerns emerge regarding implementation costs, unequal access across institutions, and risks of widening digital divides. Overall, the thematic analysis highlights both the promises and limitations of AI-driven cost reduction in higher education, offering insights for policymakers, university administrators, and educators on the economic implications of AI adoption, while also pointing to gaps that warrant further empirical research.