Design implications from research trends in generative AI for mathematics learning in higher education: A bibliometric study

This study investigates the development and thematic evolution of research on generative artificial intelligence (AI) in higher-education mathematics through a bibliometric analysis of 256 Scopus-indexed publications from 1985 to 2025. Utilizing VOSviewer and Bibliometrix, the analysis examines publication and citation trends, research collaboration, influential contributors, and thematic structures within the field. The findings demonstrate a substantial increase in both publication volume and citation frequency, particularly in recent years, indicating heightened scholarly interest in generative AI and mathematics education. The University of Sharjah emerged as the most productive institutional contributor, while the United States and China collectively accounted for a significant share of research output and citation impact. Patterns of collaboration were uneven, indicating that increased research productivity did not consistently correspond with stronger co-authorship networks. The literature reflects an interdisciplinary knowledge base connecting education, educational technology, artificial intelligence, and mathematics education. Thematic analysis identifies four interconnected areas: student-centered learning, AI in mathematics education, integrated STEM education, and teacher-focused research. These themes emphasize AI-supported learning, students' instructional experiences, technology-enhanced and active learning, interdisciplinary STEM contexts, and evolving teacher roles. This study offers a comprehensive overview of the intellectual and thematic development of research on generative AI in higher-education mathematics, highlighting both established and emerging areas of inquiry. The results provide a foundation for future research and contribute to ongoing scholarly discussions regarding the pedagogical and curricular integration of generative AI in mathematics education.