Artificial Intelligence and Cost Reduction in Public Higher Education: A Scoping Review of Emerging Evidence
School of Science and Technology, Hellenic Open University（希腊开放大学科学与技术学院）；School of Humanities, Hellenic Open University（希腊开放大学人文学院）；School of Social Sciences, Hellenic Open University（希腊开放大学社会科学学院）
Diamanto Tzanoulinou, Loukas Triantafyllopoulos, George Vorvilas, Evgenia Paxinou, Nikolaos Karousos, Thomas Dasaklis, Athanassios Mihiotis, Manolis Koutouzis, … 展开作者
Diamanto Tzanoulinou, Loukas Triantafyllopoulos, George Vorvilas, Evgenia Paxinou, Nikolaos Karousos, Thomas Dasaklis, Athanassios Mihiotis, Manolis Koutouzis, Dimitris Kalles, Vassilios S. Verykios
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.
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Metrics Research Institute and Statistics Consulting（计量研究所与统计咨询）；Department of Information Technology, Universitas Amikom Purwokerto（阿米科姆普沃克托大学信息技术系）；Department of Electrical Engineering and Information Technology, Universitas Gadjah Mada（加查马达大学电气与信息工程系）；Faculty of Computer Science, Universitas Dian Nuswantoro（迪安努桑塔罗大学计算机科学系）；Department of Health Information Management, Universitas Indonesia Maju（印尼进步大学健康管理系）
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University of South Florida（南佛罗里达大学）；University of Florida（佛罗里达大学）；Florida International University（佛罗里达国际大学）；University of Cincinnati（辛辛那提大学）；University of Alabama（阿拉巴马大学）；University of Minnesota（明尼苏达大学）；University of Michigan（密歇根大学）；Vanderbilt University Medical Center（范德堡大学医学中心）；University of Arizona（亚利桑那大学）
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Queen Mary University of London（伦敦玛丽女王大学）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Raygun Performance Monitoring（雷根性能监测公司）；Tianjin University（天津大学）；Kings Education（金斯教育）；Cymax Group Technologies（赛马克斯集团科技公司）；University of Texas at Austin（德克萨斯大学奥斯汀分校）；Indian Institute of Tropical Meteorology（印度热带气象研究所）；Microsoft（微软公司）；University of Cambridge（剑桥大学）；University of Ljubljana（卢布尔雅那大学）；Machine Intelligence Research Labs（机器智能研究实验室）；University of Western Ontario（西安大略大学）；The University of New South Wales（新南威尔士大学）；University of Birmingham（伯明翰大学）；Cardiff University（卡迪夫大学）；Vienna University of Technology（维也纳技术大学）；University of Manchester（曼彻斯特大学）；The University of Melbourne（墨尔本大学）
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Clemson University（克莱姆森大学）；University of Texas at San Antonio（德克萨斯大学圣安东尼奥分校）；University at Buffalo（布法罗大学）；North Carolina A&T State University（北卡罗来纳农工州立大学）；University of North Carolina at Greensboro（北卡罗来纳大学格林斯伯勒分校）
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Arizona State University（亚利桑那州立大学）；Makerere University（马凯雷雷大学）；Auckland University of Technology（奥克兰理工大学）；Eastern Institute of Technology（东部理工学院）；University of Glasgow（格拉斯哥大学）；University of Trento（特伦托大学）；Temple University（天普大学）；University of Exeter（埃克塞特大学）；University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）；Monash University（蒙纳士大学）；University of Toronto（多伦多大学）
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