Generative AI in University Mathematics Education Using the Theory–Context–Methodology Framework: A Systematic Literature Review
Abstract
Advances in generative AI are opening new opportunities for mathematics learning in higher education, but systematic mapping of the Theory–Context–Methodology (TCM) framework to integrate generative AI into mathematics learning remains limited. This study aims to identify the theories or conceptual frameworks, application contexts, and research methods used in studies of generative AI in higher education mathematics education. The method used is a PRISMA-based systematic literature review. The search was conducted on Scopus, Lens, and Dimensions, yielding 322 initial articles; after deduplication, screening, and eligibility assessment, 31 articles were analyzed. Article quality was assessed using the MMAT, while data were analyzed through TCM-based coding. The results indicate that generative AI is conceptualized as a pedagogical tool, a technology adoption, pedagogical competence, mathematical activity, and a cognitive partner. Its application is dominant in the classroom, general mathematics, ChatGPT, and for conceptual understanding. Methodologically, the studies were dominated by qualitative and quantitative approaches, while mixed methods and R&D/development research remained limited. These findings underscore the need for AI-based instructional designs that are critical, ethical, and supported by more integrative empirical research.
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