Problem
Research questions and friction points this paper is trying to address.
Artificial Intelligence
Cost Reduction
Public Higher Education
Financial Pressure
Equitable Access
Artificial Intelligence and Cost Reduction in Public Higher Education: A Scoping Review of Emerging Evidence
🤖 AI Summary
Public higher education institutions face mounting fiscal pressures from enrollment expansion, rising costs, and demands for equitable access, necessitating efficient cost-reduction strategies. This study presents the first systematic scoping review of empirical research on artificial intelligence–driven cost savings in higher education. Drawing on publications from Scopus and IEEE Xplore and employing thematic analysis, the review focuses on applications of generative AI, learning analytics, intelligent tutoring systems, and predictive modeling. Synthesizing findings from 21 eligible studies, the analysis reveals AI’s substantial economic potential in automating administrative processes, optimizing resource allocation, and improving student retention. However, it also highlights critical challenges, including upfront implementation costs and the risk of exacerbating digital divides. The findings offer evidence-based insights to inform education policy and strategic investment in AI technologies.
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Artificial Intelligence
cost reduction
higher education
scoping review
predictive analytics
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