Abstract

This study provides a comprehensive bibliometric mapping of the emerging research landscape on deep learning in mathematics education—an area experiencing rapid expansion yet lacking systematic synthesis. Utilizing metadata from 386 Scopus-indexed documents published between 2020 and 2025, the analysis employs VOSviewer to examine co-authorship patterns, keyword co-occurrence, and thematic clustering. Results show a sharp rise in publications, with a peak in 2024, reflecting the accelerating urgency of AI integration in educational contexts. Leading research hubs, such as the University of Auckland and Beijing Normal University, and dominant contributors from the United States and China, underline the global and collaborative nature of this field. Journal articles (40.9%) and conference papers (38.9%) constitute the primary publication formats. Four thematic clusters emerge, covering pedagogical applications, technological development, advanced approaches such as federated and personalized learning, and broader AI-in-education frameworks. The study’s novelty lies in offering the first focused bibliometric overview specifically targeting deep learning within mathematics education. Its contribution is to clarify research trends, identify influential actors, and map thematic directions, thereby providing a strategic foundation for future investigations and evidence-informed innovation in mathematics teaching and learning.

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