Artificial intelligence (AI) applications have become pervasive, influencing various domains from industries to daily life (Weng et al., 2024). The integration of AI into education has initiated transformative changes in personalized learning, virtual assistants, chatbots, and grading systems (Das, 2023; Holmes et al., 2019; Kamalov et al., 2023). It has been highlighted that AI applications hold great potential for improving teaching practices and enhancing the quality of education (Weng et al., 2024). Furthermore, the global accessibility of AI provides significant advantages to students in both developed and developing countries (Kamalov et al., 2023).

By leveraging machine learning algorithms, AI can tailor educational content to match students' individual characteristics, learning styles, and paces (Das, 2023). Personalized learning experiences support improved learning outcomes (Kamalov et al., 2023), while also saving teachers significant time (Das, 2023). Despite these benefits, several studies have emphasized potential challenges associated with AI in education, such as the need for rigorous testing and monitoring to ensure reliability (Jose & Jose, 2024), concerns regarding data privacy and security (Kamalov et al., 2023), and the possibility of reduced student engagement in active learning processes (Das, 2023).

Bibliometric analysis is a quantitative method used to summarize the intellectual structure and emerging trends within a research domain using mathematical and statistical tools (Donthu et al., 2021). This approach is typically applied to large datasets to assess and interpret research trends (Choudhri et al., 2015; Donthu et al., 2021; Gusteti & Adzkia, 2024; Linnenluecke et al., 2020; Merigó & Yang, 2017). On the other hand, systematic literature reviews focus on synthesizing findings within a specific topic or domain, often involving smaller datasets and providing more targeted insights (Linnenluecke et al., 2020; Siddaway et al., 2019; Xiao & Watson, 2019).

The existing literature includes systematic reviews (Chen et al., 2020; Kaushik et al., 2021; Opesemowo & Adewuyi, 2024; Ozay et al., 2024; Wang et al., 2024; Weng et al., 2024) and bibliometric analyses (Gusteti & Adzkia, 2024; Obreja et al., 2024; Rochman et al., 2024; Uysal et al., 2024) that explore the use of AI in education. However, there remains a need for focused bibliometric analyses in specific educational subfields.

This study aims to conduct a bibliometric analysis of research on artificial intelligence in mathematics education. It systematically examines trends, research gaps, and the current state of the field. The dataset, curated following PRISMA inclusion and exclusion criteria, ensures reliable and valid findings. The analysis is conducted using R programming and the Biblioshiny package, mapping the scientific landscape of this domain. It is anticipated that this study will provide valuable insights and guidance for future research in artificial intelligence applications within mathematics education.

This study employs a bibliometric analysis method, a quantitative research approach used to systematically examine publication trends and patterns. The primary research questions guiding this study are as follows: 1. What is the distribution of publications on artificial intelligence in mathematics education over the years? 2. Who are the most prolific authors, journals, and the most cited articles in this field? 3. What are the most frequently used keywords in the literature? 4. How do thematic maps illustrate the conceptual structure of AI in mathematics education? 5. What are the general trends and future directions in the research? The data collection and analysis process involved the following steps: • Keywords for Search: The literature search was conducted using the Scopus database with the following search string: ("artificial intelligence" OR "ai" OR "machine learning" OR "deep learning") AND ("mathematics education" OR "math education" OR "math instruction" OR "mathematical teaching") AND ("personalized learning" OR "adaptive learning" OR "intelligent tutoring" OR "learning analytics") AND ("student engagement" OR "academic performance" OR "learning outcomes" OR "assessment") AND ("educational technology" OR "e-learning" OR "digital tools") • Screening Process (PRISMA Criteria): Studies were selected based on PRISMA inclusion and exclusion criteria. Exclusion Criteria: o Non-open access studies o Non-article document types (e.g., conference papers, book chapters, books, data papers) o Studies not written in English o Studies not focused on mathematics or mathematics education o Studies not addressing artificial intelligence • Data Cleaning: A total of 557 studies were identified during the initial search. Abstracts were screened based on the inclusion and exclusion criteria, resulting in 43 full-text studies deemed eligible for further analysis. • Data Export and Analysis: The selected studies were exported from Scopus in BibTeX format for analysis. Using the Biblioshiny package in the R programming environment, a comprehensive bibliometric analysis was performed to identify publication trends, influential contributors, key themes, and emerging topics within the field. The structured methodology ensures a robust and systematic exploration of artificial intelligence research in mathematics education. By visualizing trends, mapping conceptual themes, and identifying research gaps, this study provides valuable insights to guide future research in this domain.

The bibliometric analysis conducted using Biblioshiny within the R programming environment provided insightful visualizations of publication trends, author productivity, keyword patterns, and thematic structures. The analysis of studies retrieved from the Scopus database revealed that research on artificial intelligence in mathematics education has grown significantly between 2014 and 2025. To comprehensively explore the research findings, two types of keywords (Keywords Plus and Author's Keywords) were simultaneously examined. The three-field plot results highlighted key relationships among educational technology, artificial intelligence, and personalized learning, showcasing increasing academic interest in STEM education, mathematics achievement, and the integration of AI systems. Prominent contributors in the field included Alai Rommel, Wardat Yousef, and Tashtoush Mohammad A, with highly cited articles such as Wardat et al. (2023) in the Eurasia Journal of Mathematics, Science and Technology Education drawing notable attention. In the Author's Keywords category, frequently used terms included "artificial intelligence," "mathematics," and "mathematics education," while Keywords Plus featured terms such as "students," "learning systems," and "computer-aided instruction." Thematic maps revealed that core themes like "mathematics education" and "learning systems" have shown strong development, while niche themes such as "adaptive learning systems" and "mathematics achievement" are still emerging. This study employs bibliometric analysis to comprehensively address critical aspects of artificial intelligence in mathematics education, including emerging niche themes and future expectations. By leveraging the capabilities of Biblioshiny software, the study provides a detailed understanding of AI's transformative role in education and its potential impact on mathematics education. The detailed findings, including additional visualizations and interpretations, will be presented in the full paper and presentation. This analysis offers a roadmap for future research, emphasizing the importance of AI in shaping the future of education.

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