Scholarlink

The adoption of artificial intelligence technologies—such as chatbots, intelligent tutoring systems, and predictive analytics—in higher education institutions promises increased operational efficiency, improved student support, and cost savings. However, despite growing institutional interest, there is limited empirical synthesis evaluating the actual cost-benefit balance of these innovations. This systematic review aimed to consolidate existing evidence regarding the economic and educational value of such AI-driven tools in university contexts. A comprehensive search was conducted across PubMed, Scopus, and Web of Science using a strategy structured by the PICO framework. Eligible studies were required to present original, peer-reviewed empirical data assessing both economic costs and educational benefits—such as student retention, administrative efficiency, or learning outcomes—of implementing AI systems in higher education. The PRISMA 2020 statement guided the entire methodological process. Risk of bias was assessed using the CASP tool. From 228 initial records, 221 were screened after duplicate removal, and 16 full-text studies were reviewed. However, no study met all inclusion criteria for final analysis. This absence highlights a significant gap in the current literature: while many studies address chatbot usability, learning outcomes, or satisfaction, few provide rigorous evaluations of financial or institutional returns. Emerging evidence suggests that chatbots can reduce operational costs, improve service quality, and increase student engagement, yet systematic economic evaluations remain scarce. The findings underscore the urgent need for empirical research assessing the cost-effectiveness of AI in higher education. Future studies should adopt mixed-method approaches, combining economic modeling with institutional impact assessments, and should consider user acceptance, ethical risks, and technical challenges. Addressing this gap is essential for informed, sustainable decision-making in digital transformation strategies.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Cost-Benefit Analysis of Chatbots, Intelligent Tutoring Systems, and Predictive Analytics in Higher Education: A Systematic Review

  • 会议论文

  • 发表日期：

    2026年

查看全文
  • Iván Claudio Suazo-Galdames,
  • Alain Manuel Chaple-Gil

摘要

The adoption of artificial intelligence technologies—such as chatbots, intelligent tutoring systems, and predictive analytics—in higher education institutions promises increased operational efficiency, improved student support, and cost savings. However, despite growing institutional interest, there is limited empirical synthesis evaluating the actual cost-benefit balance of these innovations. This systematic review aimed to consolidate existing evidence regarding the economic and educational value of such AI-driven tools in university contexts. A comprehensive search was conducted across PubMed, Scopus, and Web of Science using a strategy structured by the PICO framework. Eligible studies were required to present original, peer-reviewed empirical data assessing both economic costs and educational benefits—such as student retention, administrative efficiency, or learning outcomes—of implementing AI systems in higher education. The PRISMA 2020 statement guided the entire methodological process. Risk of bias was assessed using the CASP tool. From 228 initial records, 221 were screened after duplicate removal, and 16 full-text studies were reviewed. However, no study met all inclusion criteria for final analysis. This absence highlights a significant gap in the current literature: while many studies address chatbot usability, learning outcomes, or satisfaction, few provide rigorous evaluations of financial or institutional returns. Emerging evidence suggests that chatbots can reduce operational costs, improve service quality, and increase student engagement, yet systematic economic evaluations remain scarce. The findings underscore the urgent need for empirical research assessing the cost-effectiveness of AI in higher education. Future studies should adopt mixed-method approaches, combining economic modeling with institutional impact assessments, and should consider user acceptance, ethical risks, and technical challenges. Addressing this gap is essential for informed, sustainable decision-making in digital transformation strategies.