Authors: Diamanto Tzanoulinou1, Loukas Triantafyllopoulos1, George Vorvilas2, Evgenia Paxinou1, Nikolaos Karousos1, Thomas Dasaklis3, Athanassios Mihiotis3, Manolis Koutouzis2, Dimitrios Kalles1, Vassilios Verykios1
1 School of Science and Technology, Hellenic Open University, Patras, Greece
2 School of Humanities, Hellenic Open University, Patras, Greece
3 School of Social Sciences , Loukas Triantafyllopoulos, Patras, Greece
Abstract: Public higher education systems worldwide face increasing financial pressures due to expanding student populations, rising operational costs, and persistent demands for equitable access. Artificial Intelligence (AI), particularly generative AI applications such as ChatGPT, learning analytics, and intelligent tutoring systems, has been proposed as a potential driver of cost reduction and efficiency in this context.
This paper presents a scoping review of recent literature (2018–2025) examining how AI technologies contribute to economic efficiency and affordability in public universities. A structured search was conducted in Scopus using predefined terms related to AI, public higher education, and cost-related outcomes, yielding 271 documents. Following systematic screening and eligibility criteria, relevant studies were analyzed thematically.
Preliminary findings indicate that AI offers opportunities for cost savings in several areas: automating administrative tasks, optimizing resource allocation, enhancing personalized learning at scale, and reducing expenditure through predictive analytics for student performance and retention. At the same time, concerns emerge regarding initial implementation costs, equity of access across institutions, and the risk of exacerbating digital divides.
By synthesizing existing evidence, this review highlights both the promises and challenges of AI-driven cost reduction strategies in public higher education. The study aims to inform policymakers, university administrators, and educators about the economic implications of AI adoption, while also identifying gaps for future empirical research.