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.

翻译：公立高等教育体系面临日益增长的财务压力，这些压力源于学生人数的扩大、运营成本的上升以及对公平入学机会的持续需求。人工智能（AI），包括ChatGPT等生成式工具、学习分析、智能辅导系统和预测模型，已被提议作为提高效率和降低成本的手段。本研究基于Scopus和IEEE Xplore的系统检索，对人工智能在公立高等教育中应用的文献进行了一项范围综述，共识别出241篇记录，其中21项实证研究符合预设的纳入标准并进行了主题分析。研究发现显示，AI通过自动化行政任务、优化资源配置、支持规模化个性化学习以及应用预测分析提高学生保留率和机构规划来实现成本节约。与此同时，也出现了关于实施成本、机构间接入不平等以及扩大数字鸿沟风险的担忧。总体而言，主题分析突显了人工智能驱动的高等教育成本削减的前景与局限，为政策制定者、大学管理者和教育工作者提供了关于AI采用的经济影响的见解，同时也指出了需要进一步实证研究的空白。