Artificial Intelligence and Intelligent Tutoring Systems in Mathematics Education: A Bibliometric Analysis (2001–2025)

Adaptive Learning, Artificial Intelligence (AI), Bibliometric Analysis, Intelligent Tutoring Systems (ITS), Mathematics Education ALL Metrics - Views Downloads How to cite this article Kuncoro KS, Fitriasari P, Erlangga SY et al. Artificial Intelligence and Intelligent Tutoring Systems in Mathematics Education: A Bibliometric Analysis (2001–2025) [version 1; peer review: 3 approved with reservations]. F1000Research 2026, 15:107 (https://doi.org/10.12688/f1000research.176098.1) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. Export Citation Sciwheel EndNote Ref. Manager Bibtex ProCite Sente Select a format first ▬ ✚ Systematic Review [version 1; peer review: 3 approved with reservations] Krida Singgih Kuncoro https://orcid.org/0000-0001-6173-1251 1,2, Putri Fitriasari1, Sony Yunior Erlangga2,3, Marfi Ario https://orcid.org/0009-0000-5760-1681 1, Dwiani Listya Kartika1Krida Singgih Kuncoro https://orcid.org/0000-0001-6173-1251 1,2, Putri Fitriasari1, [...] Sony Yunior Erlangga2,3, Marfi Ario https://orcid.org/0009-0000-5760-1681 1, Dwiani Listya Kartika1 PUBLISHED 23 Jan 2026 Author details Author details 1 Mathematics Education, Universitas Pendidikan Indonesia, Bandung, West Java, 40154, Indonesia 2 Mathematics and Science Education, Universitas Sarjanawiyata Tamansiswa, Yogyakarta, Special Region of Yogyakarta, 55167, Indonesia 3 Science Education, Universitas Sebelas Maret, Surakarta, Central Java, 57126, Indonesia 2 Mathematics and Science Education, Universitas Sarjanawiyata Tamansiswa, Yogyakarta, Special Region of Yogyakarta, 55167, Indonesia 3 Science Education, Universitas Sebelas Maret, Surakarta, Central Java, 57126, Indonesia Krida Singgih Kuncoro Roles: Conceptualization, Methodology, Visualization, Writing – Original Draft Preparation Roles: Conceptualization, Methodology, Visualization, Writing – Original Draft Preparation Putri Fitriasari Roles: Conceptualization, Formal Analysis, Investigation Roles: Conceptualization, Formal Analysis, Investigation Sony Yunior Erlangga Roles: Conceptualization, Investigation, Writing – Review & Editing Roles: Conceptualization, Investigation, Writing – Review & Editing Marfi Ario Roles: Conceptualization, Formal Analysis, Validation Roles: Conceptualization, Formal Analysis, Validation Dwiani Listya Kartika Roles: Conceptualization, Investigation, Validation Roles: Conceptualization, Investigation, Validation OPEN PEER REVIEW REVIEWER STATUS Research on Artificial Intelligence (AI) and Intelligent Tutoring Systems (ITS) in mathematics education has expanded rapidly over the past two decades, reflecting broader advances in machine learning, learning analytics, and generative AI. However, a comprehensive overview of publication trends, thematic structures, and collaborative patterns specific to mathematics education remains limited. This study employed a bibliometric approach guided by the PRISMA protocol to systematically analyze research on AI and ITS in mathematics education published between 2001 and 2025. A total of 237 peer-reviewed journal articles and conference papers indexed in Scopus were identified, screened, and included. Bibliographic data were analyzed using the Bibliometrix R package to examine annual publication growth, author productivity, international collaboration networks, and thematic evolution through keyword co-occurrence and thematic mapping techniques. The findings indicate that research output grew slowly during the early 2000s but increased markedly after 2019, aligning with the global surge in interest in machine learning, large language models, and generative AI. Author productivity followed a typical long-tail distribution, with a small group of core authors contributing consistently over time. International collaboration was evident but uneven, dominated by a limited number of countries. Thematic analysis revealed two enduring pillars of the field: pedagogical approaches in mathematics education and technology-driven instructional systems. Emerging themes, including federated learning and adversarial machine learning, point to rising attention to privacy, security, and robustness in adaptive learning environments. STEM education and generative AI have also gained prominence in recent years. The evolution of research on AI and ITS in mathematics education reflects a clear paradigm shift from traditional instruction toward adaptive, data-driven, and intelligent learning systems. While technological innovation continues to accelerate, the findings highlight the critical role of teachers’ pedagogical readiness in ensuring that AI-enhanced mathematics education remains effective, inclusive, and sustainable. Adaptive Learning, Artificial Intelligence (AI), Bibliometric Analysis, Intelligent Tutoring Systems (ITS), Mathematics Education Corresponding Author(s) Krida Singgih Kuncoro ([email protected]) Grant information: Indonesian Education Scholarship, Doctoral Scholarship Program for Indonesian Lecturers, administered by the Center for Higher Education Funding and Assessment, Indonesian Endowment Fund for Education, and the Ministry of Higher Education, Science, and Technology of the Republic of Indonesia. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Copyright: © 2026 Kuncoro KS et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. How to cite: Kuncoro KS, Fitriasari P, Erlangga SY et al. Artificial Intelligence and Intelligent Tutoring Systems in Mathematics Education: A Bibliometric Analysis (2001–2025) [version 1; peer review: 3 approved with reservations]. F1000Research 2026, 15:107 (https://doi.org/10.12688/f1000research.176098.1) First published: 23 Jan 2026, 15:107 (https://doi.org/10.12688/f1000research.176098.1) Latest published: 23 Jan 2026, 15:107 (https://doi.org/10.12688/f1000research.176098.1) The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Over the past two decades, Artificial Intelligence (AI) has evolved from an experimental technology into a central driver of transformation in educational research and practice. Advances in machine learning, natural language processing, and student modeling have enabled the development of intelligent learning systems capable of delivering fine-grained personalization and real-time instructional adaptation at scale (Akgun & Greenhow, 2022; Walter, 2024). Importantly, this transformation is observable across both technologically advanced and resource-constrained educational contexts, indicating that AI-driven learning systems are no longer peripheral innovations but have become globally relevant pedagogical infrastructures shaping contemporary educational practices (Elimadi et al., 2024; A. Nguyen et al., 2023). In this context, AI is no longer viewed as a supplementary instructional aid; rather, it has become a defining element in the evolution of education for the twenty-first century (Tuomi, 2022; Zou et al., 2025). Among the diverse applications of AI in education, Intelligent Tutoring Systems (ITS) have emerged as one of the most theoretically grounded and empirically validated approaches to personalized instruction. Supported by decades of learning science research, ITS are designed to emulate and augment specific instructional functions of teachers by providing adaptive guidance, timely feedback, and individualized learning trajectories that dynamically respond to learners’ cognitive, affective, and behavioral states (Lin et al., 2023; Mousavinasab et al., 2021). Empirical studies conducted across varied educational contexts consistently demonstrate that ITS can deepen conceptual understanding, enhance problem-solving abilities, and reduce achievement gaps through tailored instructional support (Ateş, 2025; Ghosh, 2025). oreover, advances in learning analytics and student modeling have enabled contemporary ITS to move beyond purely cognitive optimization toward addressing affective, motivational, and metacognitive dimensions of learning, reflecting a more holistic view of student engagement and learning processes (Dever et al., 2024; Filippone et al., 2025; Stamper et al., 2024). At a systemic level, AI increasingly functions not merely as a technological enhancement but as a catalyst for reconfiguring educational paradigms, shifting instruction from standardized content delivery toward adaptive, learner-centered ecosystems (Ouyang & Jiao, 2021; Strielkowski et al., 2025; Zhong & Zhao, 2025). This paradigm shift is especially pronounced in mathematics education, a domain characterized by cumulative conceptual structures, high levels of abstraction, and persistent disparities in student achievement—conditions that render personalization pedagogically critical rather than optional (Bang et al., 2023; Gal, 2024; Pereda Loriente et al., 2025). Consequently, examining the trajectory of research on AI-driven instructional systems in mathematics education is essential not only for understanding past developments but also for anticipating how emerging technologies will continue to reshape mathematics teaching and learning. Within mathematics education, AI-driven systems address long-standing instructional challenges by aligning task difficulty, feedback, and pacing with individual learner profiles, thereby supporting students with diverse levels of prior knowledge and conceptual understanding (X. Li et al., 2024; X. Li & Zaki, 2024; Supriyadi & Kuncoro, 2023). Many students find mathematical reasoning difficult, making adaptive and personally responsive teaching approaches essential (Chiasson & Freiman, 2022). As a prominent instantiation of this approach, Intelligent Tutoring Systems (ITS) operationalize personalization through adaptive sequencing, real-time feedback, and responsive scaffolding mechanisms (D. Wang et al., 2025). These systems can adjust task difficulty, offer timely prompts, and provide focused feedback, features that, together, foster more effective and engaging learning experiences (H. Liu et al., 2024; Rodrigues et al., 2024; T. Son, 2024). The introduction of Cognitive Tutors by Koedinger and colleagues marked a foundational milestone in the field, demonstrating how cognitive modeling and knowledge tracing can be systematically operationalized within intelligent instructional systems for mathematics education. These early systems employed cognitive and knowledge-tracking models to provide targeted assistance when students encountered difficulties with mathematical tasks (Koedinger et al., 2012). Subsequent research expanded the framework by developing adaptive and affective ITSs that consider not only cognitive dimensions but also emotional, motivational, and metacognitive ones (Arroyo et al., 2014). This line of research signaled a decisive departure from rigid, linear instructional models toward adaptive systems capable of responding to heterogeneous learner needs in real time (Ingkavara et al., 2023). Beyond measurable achievement gains, AI-supported mathematics learning environments increasingly target higher-order competencies, such as problem-solving, critical thinking, and self-regulated learning, thereby aligning mathematics instruction with broader twenty-first-century educational goals (Cardona et al., 2024). These systems are capable of detecting gaps in understanding, identifying recurring misconceptions, and recommending targeted improvement strategies, transforming mathematics learning from the mere transmission of knowledge into a more interactive, diagnostic, and formative process (Ezzaim et al., 2024). In the context of international curricula that prioritize numeracy and mathematical reasoning, AI-based tools help transform learning from the mere transmission of knowledge to a more interactive, diagnostic, and formative process (Cho & Kim, 2025). These contributions underscore the growing importance of AI-based instructional systems in addressing global demands for equitable, effective, and sustainable mathematics education. Despite the rapid expansion of research on AI in education, systematic bibliometric analyses that explicitly focus on the intersection of Artificial Intelligence, Intelligent Tutoring Systems, and mathematics education remain limited. Existing bibliometric studies have largely examined AI in education from broad perspectives, often emphasizing technologies such as virtual and augmented reality, gamification, massive open online courses, or learning analytics, while giving comparatively less attention to mathematics education as a primary domain for intelligent tutoring and adaptive learning (Deda & Disnawati, 2025; S. Sun et al., 2021; Zawacki-Richter et al., 2019). More critically, these studies rarely capture long-term thematic evolution, domain-specific research dynamics, or emerging concerns related to ethical AI, data privacy, and generative models within mathematics-focused instructional systems. Narrative reviews have offered more focused theoretical perspectives on ITS and adaptive learning, particularly through frameworks such as Knowledge–Learning–Instruction (KLI) and syntheses of lessons learned from decades of ITS implementation (Aleven et al., 2016; Koedinger et al., 2012). More recent reviews further highlight the effectiveness of ITS in supporting mathematics instruction and student engagement (Arroyo et al., 2014; Walkington et al., 2024). However, the qualitative and narrative nature of these reviews constrains their capacity to provide systematic, quantitative insights into global publication trends, international collaboration networks, and longitudinal thematic shifts within the field. These limitations underscore the need for a comprehensive, data-driven bibliometric analysis capable of quantitatively mapping the intellectual structure, research dynamics, and thematic evolution of AI and Intelligent Tutoring Systems in mathematics education. Accordingly, the present study conducts a large-scale bibliometric analysis of Scopus-indexed publications published between 2001 and 2025. The analysis focuses on four interrelated dimensions: (1) publication growth over time; (2) patterns of author productivity examined through Lotka’s law; (3) international collaboration reflected in national-level contributions and cross-border research networks; and (4) thematic and conceptual evolution analyzed through keyword co-occurrence, thematic mapping, and conceptual structure analysis. Together, these dimensions provide empirical evidence that not only documents the historical development of AI and ITS research in mathematics education but also offers strategic insights to inform future research agendas, instructional design, and educational policy in technology-enhanced mathematics learning. This study applied a bibliometric method framed by the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) protocol to maintain transparency and reproducibility in every stage of the process. PRISMA guidelines were used to systematically record the identification, screening, and selection of literature (Page et al., 2021). The sequence of this selection is illustrated in Figure 1. The research design followed five key steps: (1) conduct a comprehensive search of the Scopus database, (2) refine and screen the dataset using inclusion and exclusion criteria, (3) organize bibliographic information into a structured dataset, (4) perform analysis using bibliometric tools, and (5) synthesize the results using descriptive statistics and science mapping techniques. The search strategy was developed to capture research at the intersection of Artificial Intelligence (AI), Intelligent Tutoring Systems (ITS), and mathematics education. To do so, a specific query was run on the Scopus database in July 2025: (TITLE-ABS-KEY (“adaptive learning” OR “intelligent tutoring system*” OR “personalized learning” OR “adaptive feedback” OR “student modeling”) AND TITLE-ABS-KEY (“mathematics education” OR “mathematics learning” OR “mathematics instruction”)) AND (PUBYEAR > 2000) AND (LIMIT-TO (DOCTYPE, “ar”) OR LIMIT-TO (DOCTYPE, “cp”)) AND (LIMIT-TO (LANGUAGE, “English”)) The initial search yielded 239 documents published between 2001 and 2026. Of these, one article published in 2026 was excluded as it fell outside the analysis range (2001–2025), and one Spanish-language article was excluded for not meeting the language criterion. The final dataset therefore consisted of 237 documents, including journal articles and conference proceedings. Only peer-reviewed publications were included, while book chapters, editorials, and non-scholarly documents were excluded. Scopus was selected as the sole database due to its extensive coverage of reputable journals in education, computer science, and related interdisciplinary fields (Pranckutė, 2021). The screening process was carried out in three stages. First, duplicates and inconsistent metadata (e.g., variations in author names, affiliations, or keywords) were identified and removed using OpenRefine 3.7.6. Second, documents irrelevant to mathematics education or not explicitly addressing AI/ITS were excluded. Third, ambiguous records were manually reviewed to ensure alignment with the study’s focus. Following this rigorous cleaning process, 237 valid documents were retained for bibliometric analysis. Bibliographic information, including authors, titles, abstracts, keywords, affiliations, sources, and references, was exported in CSV format. To ensure reliability, the dataset was cross-verified with Scopus records and checked independently by two researchers. Author keywords (DE) and Keywords Plus (ID) were standardized through clustering, merging terms with equivalent meanings (e.g., “mathematics learning” vs. “mathematical learning,” “educational technology” vs. “educational technologies”). All derived bibliometric data and analytical outputs generated during this process are openly available in Zenodo (Kuncoro et al., 2025). The cleaned dataset was analyzed using the Bibliometrix R-package (v.4.2.2) and its Biblioshiny web interface (Aria & Cuccurullo, 2017). The analyses included: 1. Descriptive analysis of annual scientific production, source journals, authorship patterns, and citation impact. 2. Productivity analysis based on Lotka’s Law (author productivity distribution). 3. Collaboration network analysis at the author, institutional, and country levels to map international partnerships. 4. Science mapping, including keyword co-occurrence analysis, thematic mapping, thematic evolution, and factorial analysis (conceptual structure mapping). This review adhered to the PRISMA 2020 reporting guidelines, and the completed PRISMA checklist and flow diagram are publicly available in Zenodo (Kuncoro et al., 2026). To ensure transparency and reproducibility, all search queries, inclusion/exclusion criteria, and data-cleaning procedures were documented. The combination of Scopus indexing with bibliometric software ensured validity and comparability with other large-scale analyses in the field of educational technology (Donthu et al., 2021). This bibliometric analysis covers 237 documents published across 160 different sources during the period 2001–2026. The relatively high ratio between the number of documents and sources indicates that research in this field is disseminated through diverse publication outlets, including journals in education, computer science, psychology, and mathematics. This finding confirms the interdisciplinary nature of research focusing on Artificial Intelligence (AI), Intelligent Tutoring Systems (ITS), adaptive learning, and mathematics education. A summary of the dataset’s main information is presented in Table 1. The average age of the documents (4.89 years) shows that this collection is relatively recent, while the average of 16.55 citations per document reflects a fairly strong level of impact. The total number of references reached 8,775, or about 37 references per document, indicating that the research is grounded in a broad and comprehensive body of literature. In terms of content, there are 924 Keywords Plus and 612 Author’s Keywords, reflecting thematic diversity. Keywords Plus illustrates the broad scope of the field, whereas Author’s Keywords capture more specific research focuses, ranging from ITS, adaptive learning, and artificial intelligence to mathematics education. Author involvement also indicates a high degree of collaboration. A total of 702 authors contributed to the 237 documents, averaging 3.45 authors per document. Only 38 documents were single-authored, highlighting that this research is largely collaborative. International collaboration accounted for 18.57%, which, although not dominant, demonstrates cross-country networking in this field. The distribution of document types shows a balance between journal articles (132 documents) and conference proceedings (105 documents). Journal articles reflect more in-depth contributions that have undergone peer review, whereas conference proceedings highlight the need to disseminate emerging findings quickly within dynamic communities such as AI and ITS. Annual scientific production in this field reveals a clear trajectory from slow growth toward more rapid expansion. Between 2001 and 2010, the number of publications was still relatively low and sporadic, rarely exceeding five documents per year, reflecting the early stage of research on Artificial Intelligence (AI) and Intelligent Tutoring Systems (ITS) in mathematics education. A more notable increase began in 2012, marking the onset of more sustained academic interest. After 2015, annual output stabilized within the range of 7–13 publications, indicating moderate but consistent growth. This development is illustrated in Figure 2. A significant turning point appeared after 2019, when the number of publications rose more sharply, surpassing 20 documents in 2021. This upward trend peaked in 2024 with nearly 50 publications, representing the highest figure across the entire observation period. The surge coincided with the global boom in AI-based research, particularly the application of machine learning and large language models in education. The apparent decline in 2025–2026 should be interpreted with caution, as it is more likely due to indexing delays of recent publications than to a real decrease in research activity. The findings suggest that research on AI and ITS in mathematics education has evolved from a relatively marginal topic in the early 2000s into a rapidly expanding field over the past decade. This pattern reflects Price’s Law, which anticipates exponential growth in publications within emerging disciplines, and it highlights the increasing significance of adaptive and intelligent technologies in shaping the future of mathematics teaching and learning (Cotič et al., 2024; Samala et al., 2024). The Three-Field Plot illustrates how authors (AU), their countries of origin (AU_CO), and research keywords (DE) intersect within studies on Artificial Intelligence (AI), Intelligent Tutoring Systems (ITS), and mathematics education. On the author side, key contributors include Fancsali S. E., Ritter S., Aleven V., McLaren B. M., Arroyo I., Berman S. R., Koedinger K. R., Hausmann R. G. M., Woolf B. P., Borchers C., and Hu X., who stand out as leading figures in the field. The majority of authors are closely connected with institutions in the United States, while figures such as Hausmann RGM and Borchers C are affiliated with Germany, and Hu X is linked to China. These connections are illustrated in Figure 3. In the country column (AU_CO), the USA dominates research productivity, followed by China and Germany. Other countries such as Sri Lanka, Turkey, Colombia, Italy, Mexico, Portugal, Spain, India, the United Kingdom, Indonesia, and Australia also contribute, though in smaller numbers. The connections between countries and keywords indicate that the USA and China are most frequently associated with core themes such as mathematics education, intelligent tutoring systems, personalized learning, and adaptive learning. From the keyword side (DE), mathematics education, intelligent tutoring systems, personalized learning, and adaptive learning emerge as the most dominant research themes. In addition, keywords such as mathematics learning, artificial intelligence, problem solving, and mathematical modelling appear frequently. Newer terms such as large language models and ChatGPT have also begun to surface, though with lower frequency, signaling the emergence of the latest research trends in this field. The Authors’ Production Over Time plot represents the publication trajectories of leading scholars in AI and Intelligent Tutoring Systems (ITS) for mathematics education. Each horizontal line shows a researcher’s active publishing period, while bubbles mark the number of articles per year. Larger bubbles represent higher productivity, whereas darker colors indicate higher annual citation impact (TC/year). This visualization is presented in Figure 4. Figure 4 reveals several key patterns. First, senior authors such as Koedinger KR and Aleven V demonstrate long publication spans—2007–2023 and 2009–2024 respectively—with consistent contributions. Second, authors with more concentrated impact periods, such as Arroyo I (2011–2023, peaking in 2014 with the highest TCpY of 17.5) and Hu X (2013–2018, with medium to high impact publications), show significant productivity within narrower timeframes. Third, there is a group of authors with productivity concentrated in specific years, including Fancsali SE, Ritter S, and Berman SR, who contributed mainly during 2018–2019 on formative assessment in ITS. Fourth, newer authors such as Borchers C (active since 2023, co-publishing with Aleven and Koedinger) and Jia J (steady since 2016, peaking in 2022 with TCpY of 8.5) reflect the emergence of a new generation of researchers. Meanwhile, Albano G shows sporadic activity (2011–2019) with contributions such as the integration of GeoGebra in e-learning (2019, TCpY 3.7). Figure 5 shows that the distribution of author productivity follows the characteristic pattern of Lotka’s Law. The majority of authors produced only a single publication, accounting for more than 60%. The number of authors with two publications declines sharply, and even fewer authors generated three or more publications. The observed curve (solid line) aligns with the theoretical Lotka curve (dashed line), though slight deviations are visible in the categories of authors with one or two publications. Overall, this distribution forms a long-tail pattern, where most contributions come from low-productivity authors, while only a small core group of authors consistently contribute multiple publications. The findings in Figure 5 confirm the relevance of Lotka’s Law in the bibliometric context of AI and ITS research in mathematics education. They also indicate that this field is largely shaped by widespread contributions from sporadic authors, with a small nucleus of highly productive authors maintaining the continuity of research. Figure 6 presents the distribution of publications based on the country of the corresponding author, distinguishing between Single Country Publications (SCP) and Multiple Country Publications (MCP). The chart shows that the United States (USA) dominates with the largest contribution (32 articles), followed closely by China (30 articles), while Germany ranks third with 10 articles. Countries with medium-level contributions include Turkey (9 articles), Indonesia (8 articles), the United Kingdom (6 articles), Italy (5 articles), and Australia, South Korea, and Malaysia (4 articles each). Other countries such as Mexico, Spain, India, the Netherlands, Norway, the Philippines, Thailand, Belgium, Colombia, and Finland contributed smaller numbers of publications. In terms of collaboration patterns, the USA and China produced more domestic publications (SCP), while Germany showed a balance between SCP and MCP with a high ratio of international collaboration. Some smaller contributors, such as Belgium, published exclusively MCP articles, indicating a complete orientation toward collaboration. The findings in Figure 6 highlight that research on AI and ITS in mathematics education is global in nature—dominated by major countries but also strengthened by significant contributions from developing nations through international collaboration. The Trend Topics chart in Figure 7 illustrates the evolution of research terms from 2010 to 2024 in the fields of Artificial Intelligence, Intelligent Tutoring Systems, adaptive learning, and mathematics education. Bubble size reflects the annual median frequency of a term, while the horizontal line indicates the interquartile range (IQR), representing the period of emergence and consistency of each topic. In the early phase (2010–2017), terms that appeared were mainly associated with conventional educational technologies such as cognitive systems, educational technology, computer-aided instruction, e-learning, as well as subject-specific topics like algebra and mathematical techniques. Research also focused on specific groups, such as middle school students. In the middle phase (2018–2020), research became more consolidated, with a rise in pedagogically oriented terms including intelligent tutoring systems, mathematics instruction, mathematics learning, and online learning, which began to dominate. The term collaborative learning also emerged during this period. In the most recent phase (2021–2024), there was a sharp increase in terms such as artificial intelligence and personalized learning, with the largest bubbles across the entire timeline, marking a clear shift in research focus. STEM education also gained strength, reflecting a broader interdisciplinary perspective. By 2023–2024, terms such as teachers and personnel training appeared, indicating a growing awareness of the role of teachers and the importance of professional development. Several terms remained consistent throughout the period, particularly mathematics education, intelligent tutoring systems, and online learning, which continue to be central in this research domain. These findings confirm that Figure 7 represents a transition in research focus from conventional instruction toward the integration of AI and personalized learning. The keyword co-occurrence network analysis in Figure 8 provides a visual representation of the cognitive structure of research in Artificial Intelligence (AI), Intelligent Tutoring Systems (ITS), adaptive learning, and mathematics education. Larger nodes represent keywords with higher frequency, while edges indicate the strength of connections between keywords. Node colors represent thematic communities formed through clustering algorithms. Four dominant keywords were identified: mathematics education, students, intelligent tutoring system, and computer-aided instruction. These four terms form the core of the network and act as cross-theme connectors. Strong associations are especially evident in the pairs mathematics education–students and intelligent tutoring system–computer-aided instruction, reflecting close conceptual linkages in the literature. In addition, the network reveals four main clusters: 1. Red cluster – focused on pedagogy and educational practice, with keywords such as students, teaching, curricula, artificial intelligence, and collaborative learning. 2. Green cluster – centered on instructional technology and learning systems, with keywords such as intelligent tutoring system, computer-aided instruction, education computing, learning systems, and problem-solving. 3. Blue cluster – highlighting AI frontier technologies, including personalized learning, adaptive learning systems, federated learning, adversarial machine learning, and self-supervised learning. 4. Purple cluster – representing the integration of mathematics education with STEM, featuring terms such as science education, technology education, and engineering and mathematics. The findings in Figure 8 emphasize that research on AI and ITS in mathematics education has a complex thematic structure, with a strong central core while simultaneously expanding toward cutting-edge technological frontiers. The Thematic Map in Figure 9 illustrates the distribution of research themes across two main dimensions: relevance (centrality) and level of development (density). From the analysis, four theme categories can be identified. First, Niche Themes (upper-left quadrant) include topics with high density but low relevance. Themes such as generative AI, multimodal, learning experiences, differentiation (calculus), natural language processing systems, and automated assessment fall into this group. These represent deep and focused research but remain limited in scope. Second, Motor Themes (upper-right quadrant) are characterized by both high centrality and density, marking them as the driving forces of research. Keywords such as engineering education, curricula, and science technologies appear dominant. This highlights the key role of engineering education and science–technology integration in shaping current research developments. Third, Emerging or Declining Themes (lower-left quadrant) consist of topics with low density and low relevance. Examples include Bayesian networks, student modeling, and computer simulation. Their position indicates that these themes are either in the early stages of development or are losing relevance. Fourth, Basic Themes (lower-right quadrant) are defined by high relevance but low density. Key keywords here include students, mathematics education, teaching, computer-aided instruction, intelligent tutoring system, education computing, and advanced AI methods such as federated learning and adversarial machine learning. The findings in Figure 9 confirm that research on AI and ITS in mathematics education combines well-established themes, driving themes, and rapidly developing frontiers, offering a comprehensive picture of the dynamics and future direction of the field. The thematic evolution analysis in Figure 10 highlights shifts in research focus in Artificial Intelligence (AI), Intelligent Tutoring Systems (ITS), adaptive learning, and mathematics education from 2001 to 2026. 1) 2001–2015 was dominated by themes such as computer-aided instruction, educational technology, and mathematical instruction, supported by terms like competence and intelligent vehicle highway systems. This reflects the exploratory phase of using computers in mathematics teaching. 2) 2016–2020 saw research consolidation, with mathematics education emerging as a central theme, connected to mathematical modelling, learning environments, and personalized learning. Themes like cognitive tutor and data mining also appeared, signaling the early adoption of more adaptive AI approaches. 3) 2021–2023 marked a transition with the strengthening of intelligent tutoring systems and mathematics education. New themes such as chatbots, collaborative learning, curricula, robotics, and teaching indicated expansion into online learning and deeper AI integration. 4) 2024 showed diversification into broader STEM areas with themes like geometry and engineering and mathematics, while personalized learning and online learning remained consistent. 5) 2025–2026 revealed the emergence of frontier topics such as adversarial machine learning, integration, and science technologies. Nevertheless, mathematics education and intelligent tutoring systems persisted as anchor themes. Overall, Figure 10 demonstrates that mathematics education and intelligent tutoring systems have remained consistent throughout, serving as the backbone of research evolution even as surrounding technological contexts shifted significantly. The Factorial Analysis in Figure 11 presents a conceptual structure map explaining the intellectual framework of research on Artificial Intelligence (AI), Intelligent Tutoring Systems (ITS), adaptive learning, and mathematics education. Two main dimensions—Dimension 1 (40.08%) and Dimension 2 (30.93%)—together account for 71.01% of the data variance, indicating a robust conceptual structure. Several main clusters were identified: 1) Advanced AI and Intelligent Tutoring Systems Cluster (upper-left quadrant): Includes terms such as large language models, intelligent tutoring, intelligent tutoring system, and semantics. This cluster represents cutting-edge research integrating advanced AI models with intelligent learning systems. 2) Digital Pedagogy and Mathematics Education Cluster (lower-left quadrant): Covers terms such as mathematics instruction, online learning, e-learning, and educational technology. This cluster emphasizes the application of digital technologies to mathematics learning. 3) STEM Education and Emerging AI Technologies Cluster (right quadrant): Includes science education, engineering education, science technologies, machine learning, federated learning, and teachers. This reflects the link between STEM education and the use of advanced AI algorithms, as well as the role of teachers in integrating them. In addition, central keywords near the origin, such as artificial intelligence, students, teaching, and computer-aided instruction, represent the common foundation of research. Meanwhile, peripheral terms such as large language models, federated learning, science technologies, and online learning point to specialized, emerging, or frontier areas. The findings in Figure 11 confirm that the conceptual structure of this research field is anchored in well-established foundations while also extending toward rapidly evolving frontiers aligned with the latest AI trends. The bibliometric results show that research in Artificial Intelligence (AI), Intelligent Tutoring Systems (ITS), adaptive learning, and mathematics education has developed dynamically with a strong interdisciplinary character, involving computer science, education, psychology, and mathematics. The main information findings in Table 1 indicate that this research collection is relatively recent, with an average document age of less than five years, and has significant impact, with an average of more than 16 citations per article. This confirms that publications in this field provide not only theoretical relevance but also tangible practical contributions. Authorship patterns reveal strong collaboration at both national and international levels, though there is still room to strengthen global networks. The distribution between conference proceedings and journal articles also reflects the dual character of the field: a drive for rapid dissemination through conferences alongside a commitment to deeper, peer-reviewed contributions in journals. Viewed over time in Figure 2, research growth reveals distinct stages of development. Between 2001 and 2010, publications appeared only sporadically, reflecting an early phase in which the use of AI and STI in mathematics education was mainly limited to small-scale trials. From 2012 onwards, output increased more steadily, marking the first signs of consolidation. A decisive turning point occurred after 2019, coinciding with rapid progress in machine learning and, in particular, the emergence of large language models (LLMs), which drew global attention to the potential of AI in education. Recent studies highlight how AI-driven scaffolding, adaptive feedback, and personalized learning approaches can improve mathematics learning outcomes (Horvers et al., 2025; H. Li et al., 2025b; Yohannes & Chen, 2024). The surge in publications—reaching nearly 50 in 2024—mirrors the trajectory predicted by Price’s Law, which holds that emerging research fields expand exponentially until they mature. Within this framework, AI and ITS have moved beyond their early niche status to become an increasingly recognized and established area of international research. The fact that recent trend topics have been dominated by keywords such as artificial intelligence, personalized learning, and mathematics education further reinforces the argument that the field has undergone substantial transformation and is moving toward a stage of research maturity. The implication is that future research will likely place greater emphasis on diversifying AI applications, including the use of multimodal intelligent tutors, adaptive systems powered by big data, and the integration of generative AI in test construction, hint provision, and the design of interactive tutoring systems. Thus, the annual publication trend reflects not only an increase in research quantity but also a paradigmatic transformation in the ways AI is being used to shape the future of mathematics education. The Authors’ Production over Time analysis in Figure 4 highlights diverse patterns of contribution within the AI and ITS research community in mathematics education. Senior figures such as Aleven and Koedinger demonstrate long-term continuity in productivity, reflecting their roles as thought leaders who have shaped the field’s trajectory from its early stages to contemporary issues, including the integration of tutoring and mentoring roles in intelligent learning systems (Borchers et al., 2025; L. N. Son et al., 2025). By contrast, authors with shorter but high-impact periods of contribution—such as Arroyo, Hidayat, and Nygren—demonstrate that innovations addressing cognitive, metacognitive, and affective dimensions can have a strong influence even within a limited timeframe (Arroyo et al., 2014; Hidayat et al., 2023; Nygren et al., 2019). Figure 4 shows the collaboration between Fancsali, Ritter, and Berman, illustrating the value of team research, especially in promoting ITS-driven formative assessment, which remains a cornerstone of adaptive education (Fancsali et al., 2018; Fancsali & Ritter, 2019; Fisher et al., 2020; Ritter et al., 2019; Zheng et al., 2019). At the same time, new contributors such as Borchers and Jia signal a generational shift in the field, drawing attention to cutting-edge technologies such as large language models (LLMs), intelligent proctoring, and adaptive prompt design (Borchers et al., 2023, 2025; Jia et al., 2020, 2023; Jia & He, 2022; Jia & Miao, 2021; Jia & Yu, 2017; H. T. Nguyen et al., 2024). These advances suggest that the field is expanding not only in scale but also in focus, thanks to sustained contributions from senior scholars, the impact of specific studies, and the incorporation of new researchers who push the technological boundaries (D. Li et al., 2025a). An analysis of author productivity using Lotka's Law confirms this distributional pattern, as shown in Figure 5. The majority of publications come from researchers with only one or two articles, while a smaller group—including Aleven, Koedinger, Arroyo, and Hu—maintains a consistent output over the long term. This is consistent with Lotka's prediction that a handful of highly productive authors dominate output, while the majority contribute intermittently. The scholarly implications of these findings are notable. First, international collaboration needs to be strengthened to reduce dependence on a small group of senior authors. Second, the contributions of these established researchers must be sustained and extended through broader networks and generational renewal, as their work continues to shape both theory and methodology. Third, the large proportion of new authors demonstrates that the field remains open, offering significant opportunities for early-career researchers. Finally, creating a balanced research ecosystem—where widespread participation is complemented by the depth of work of lead authors—will require deliberate mentoring and capacity-building strategies to ensure long-term productivity. Taken together, the analysis of RQ2 shows that author productivity in AI and ITS research within mathematics education reflects not only the distribution of publications but also the broader social and academic structures that guide the growth of the field (Borchers et al., 2025; D. Li et al., 2025a; H. Liu et al., 2024; Lyu et al., 2025; H. T. Nguyen et al., 2024). The examination of international collaboration through the Three-Field Plot and Corresponding Author’s Countries reveals that progress in Artificial Intelligence (AI), Intelligent Tutoring Systems (ITS), adaptive learning, and mathematics education has been driven not only by individual scholars but also by increasingly interconnected global networks (See Figure 3). Findings suggest that research activity is still concentrated in a few major countries, particularly the United States, China, and Germany. The United States occupies a leading position, contributing extensively to central themes such as mathematics education, ITS, personalized instruction, and adaptive learning. China ranks just behind, showing a comparable research profile that reflects its rapid growth in AI-based educational technologies. Germany also plays a pivotal role, serving as a hub for cross-border collaboration that strengthens the reach and visibility of European contributions on the global stage. From a topic perspective, the predominance of keywords such as mathematics education and intelligent tutoring systems highlights the global focus on leveraging AI to enhance mathematics learning. However, the emergence of new keywords such as large language models and ChatGPT reflects a shifting trend toward integrating generative AI into ITS (Chohan & Khan, 2024; J. Liu et al., 2025; Ru & Li, 2025; Xuan et al., 2025; Yunianto et al., 2024). This indicates that while the core topics remain relatively stable, the research community is also open to cutting-edge innovations such as conversational tutors, automatic problem generation, and adaptive feedback. Therefore, these collaboration patterns and topic orientation affirm that the field is dynamic and continuously evolving alongside cutting-edge technological developments. An analysis of the corresponding authors' countries in Figure 6 reveals distinctive contribution patterns. The United States and China lead the publication volume; however, their high proportion of Single Country Publications (SCPs) shows that much of this work is domestically focused. While this reflects strong domestic research capacity, the relatively low proportion of Multi-Country Publications (MCPs) suggests that their global influence could grow further through broader international partnerships. Germany, in contrast, stands out for its high degree of cross-border collaboration, with a SCP share of 50%. While its overall output is lower, its openness to cooperation generates more diverse perspectives and greater international visibility. Similar trends are observed in Belgium, Malaysia, Spain, and Finland, where a relatively modest number of publications is offset by a strong commitment to collaboration. Countries with average research output, such as Indonesia, Turkey, and the United Kingdom, also play a prominent role. Indonesia and Turkey produce primarily domestic studies, but their growing publication trends point to great potential for greater global engagement. The United Kingdom, on the other hand, records a comparatively high rate of MCPs, allowing it to act as a bridge between major research centers and broader academic communities. Taken together, the findings from RQ3 indicate that research on AI, ITS, and mathematics education is driven not only by major players such as the United States and China, but also by countries that prioritize collaboration. This pattern highlights the importance of international networks for the advancement of ITS and adaptive learning, as they facilitate the exchange of ideas, technologies, and pedagogical approaches on a global scale. For developing countries, active participation in such networks is particularly vital to strengthen research capacity, increase visibility, and ensure that AI-based educational innovations remain inclusive and globally relevant. The progression of research topics reveals a clear paradigm shift: from conventional technology-based instruction to Artificial Intelligence (AI) and personalized learning as the new foundation (see Figure 7). Early studies focused mainly on computer-assisted instruction in specific subjects such as algebra. In the intermediate phase, attention shifted to adaptive learning systems, with greater emphasis on mathematical modeling and cognitive tutors. A major turning point emerged after 2021, when AI-based tutors and personalized learning approaches became dominant, underscoring the central role of AI in data-driven adaptive systems (Alvarez, 2024; Walkington et al., 2024). The growing importance of STEM education further reflects a trend toward interdisciplinary approaches, which place mathematics in a broader framework, aligned with the competencies required in the 21st century (Barana et al., 2024). Another notable trend is the increasing use of terms such as teacher and staff training, which highlights a growing awareness that successful technology integration depends largely on teachers' ability to manage and facilitate digital learning (D. Li et al., 2025a).

co-occurrence analysis also sheds light on the structure of the field, as shown in Figure 8. Two main pillars stand out: pedagogical practices and technology-based teaching systems. Within this framework, the red cluster highlights teaching strategies, while the green cluster emphasizes teaching technologies that support student learning. The blue cluster points to new research frontiers, with studies exploring areas such as federated learning and adversarial machine learning, indicating a growing concern for privacy and security in adaptive systems (H. T. Nguyen et al., 2024; R. Wang & Wu, 2023). On the other hand, the purple cluster points to the expansion of work in global STEM contexts, showing that ITS are no longer considered in isolation, but as part of a broader movement to strengthen scientific and technological literacy (Le & Taherdoost, 2025). Together, these patterns confirm the transition from traditional instruction to an AI-powered STEM education ecosystem that values both inclusivity and adaptability. The thematic map in Figure 9 highlights the diversity of AI and ITS research in mathematics education across four categories. First, specific topics such as generative AI and multimodal learning represent specialized but rapidly expanding frontiers, particularly in the design of multimodal learning experiences (D. Li et al., 2025a). Second, key topics such as engineering education and science technologies act as cohesive hubs of innovation, driving the integration of AI into STEM curricula (Portillo & Alvarado, 2025). Third, basic themes including students, mathematics education, and intelligent tutoring systems serve as foundational anchors, showing continuity from earlier research to present adaptive approaches (Horvers et al., 2025). Fourth, Emerging or Declining Themes show the decline of student modeling and Bayesian networks, signaling a transition toward newer methods or potential revitalization if combined with frontier AI. Accordingly, the thematic map functions as a diagnostic tool to identify mature areas, promising frontiers, and shifting themes. Longitudinally, the field has transitioned from simple instructional experiments to frontier AI-driven ecosystems (see Figure 10). In the early phase (2001–2015), research focused on computer-aided instruction emphasizing digital instructional tools (Arroyo et al., 2014; Craig et al., 2013; Ersoy & Akbulut, 2014; Harskamp & Suhre, 2006; Jurkovic, 2001; Kinnari-Korpela, 2015; Underwood, 2007). The consolidation phase (2016–2020) marked a turning point with mathematics education, cognitive tutors, and data mining gaining prominence, shifting the orientation toward data-driven adaptive systems (Albano & Dello Iacono, 2019; Greefrath, 2020; Nabiyev et al., 2016; Nygren et al., 2019; Wu, 2019). The transition phase (2021–2023) saw the integration of ITS and AI with topics like chatbots and collaborative learning, expanding approaches toward social and collaborative dimensions (Chau et al., 2025; Lee & Yeo, 2022; Neto & Fernandes, 2019; Rodríguez-Martínez et al., 2023; S. Sun et al., 2023; Taranto et al., 2024). The contemporary phase (2024) reflects the adoption of generative AI tools such as ChatGPT and the diversification of research into STEM areas, including geometry and engineering education (Chohan & Khan, 2024; Copur-Gencturk et al., 2024; Murdan, 2024; Opesemowo & Ndlovu, 2024; Torres-Peña et al., 2024). Looking ahead, the prospective phase (2025–2026) is expected to focus on frontier topics such as adversarial machine learning and system integration, with strong emphasis on security, privacy, and multimodal learning (Borchers et al., 2025; Cosentino et al., 2025; H. Li et al., 2025b; J. Liu et al., 2025; Lyu et al., 2025; C. Sun et al., 2025). Taken together, this trajectory illustrates the field’s progression from early digital tools to advanced intelligent systems that respond to the complex challenges of the generative AI era. The factorial analysis as shown in Figure 11, uncovers layered conceptual structures within AI and ITS research. The first dimension illustrates the shift from specific pedagogical research such as mathematics instruction toward broader integration in STEM education (Barana et al., 2024). The second dimension distinguishes between foundational pedagogy-based research such as online learning and frontier AI-based research such as large language models (Alvarez, 2024). Established areas continue to include ITS, e-learning, and mathematics instruction, while emerging areas include LLMs and federated learning, now being explored in adaptive contexts (H. T. Nguyen et al., 2024). Cross-theme interactions highlight the importance of keywords such as artificial intelligence and teachers as connectors between pedagogy and frontier technology (X. Li et al., 2024). Strategically, four implications arise: (1) integrating advanced AI into ITS, (2) strengthening the synergy between pedagogy and frontier AI, (3) enhancing teacher professional development for technological adaptation, and (4) expanding personalized learning within STEM contexts more holistically (Le & Taherdoost, 2025). Thus, factorial analysis affirms that the future of research will be determined by a balance between technological sophistication and pedagogical needs. Despite these contributions, several limitations should be noted. First, this study relied on a single database (Scopus) and restricted inclusion to English-language journal articles and conference papers, which may underrepresent relevant work indexed elsewhere or published in other languages and formats; moreover, recent-year patterns (especially 2024–2025) may be affected by indexing delays, and bibliometric indicators cannot directly evaluate instructional effectiveness, learning outcomes, or causal mechanisms. Second, science-mapping results depend on author keywords and metadata quality; variations in terminology (e.g., ITS vs. adaptive learning systems, LLM vs. generative AI) may influence cluster formation and thematic evolution. These constraints imply that the findings should be interpreted as a macroscopic “research landscape” rather than a substitute for pedagogical impact evidence, yet they still offer actionable implications: (i) scholars can use the identified thematic pillars and emerging fronts to position novelty and avoid duplication, (ii) institutions and funders can target capacity building in under-connected regions to strengthen international collaboration and knowledge transfer, and (iii) policymakers and practitioners should treat teacher professional development, governance, and responsible AI (privacy, security, robustness) as co-requisites of scaling AI/ITS in mathematics education rather than afterthoughts. Accordingly, further research is recommended to (1) expand coverage using multi-database designs (e.g., Scopus + WoS + ERIC) and multilingual retrieval, (2) integrate bibliometrics with systematic or scoping reviews and, where feasible, meta-analytic evidence to link research growth with demonstrated learning benefits, (3) conduct comparative empirical studies on generative-AI-enabled tutoring (e.g., feedback quality, misconception diagnosis, equity effects, and classroom orchestration), and (4) develop shared benchmarks and reproducible evaluation protocols that explicitly test privacy-preserving and security-aware approaches (e.g., federated learning, adversarial robustness) in authentic mathematics learning settings. This study did not involve human participants, animals, or any identifiable personal data. All analyses were conducted using secondary data obtained from publicly accessible bibliographic records. Therefore, ethical approval and informed consent were not required. The raw bibliographic records analyzed in this study were retrieved from the Scopus database and are subject to third-party licensing restrictions; therefore, they cannot be shared publicly. To ensure transparency and reproducibility, all derived bibliometric data and analytical outputs generated using the Biblioshiny interface of the Bibliometrix R package have been deposited in Zenodo. These include summary bibliometric indicators, annual publication counts, author productivity distributions, country-level contribution tables (SCP/MCP), keyword trend data, co-word co-occurrence matrices, thematic map coordinates, and factorial analysis results. • Zenodo. Bibliometric data and analysis files for “Artificial Intelligence and Intelligent Tutoring Systems in Mathematics Education: A Bibliometric Analysis (2001–2025)”. DOI: https://doi.org/10.5281/zenodo.18005367 (Kuncoro et al., 2025) • Zenodo. PRISMA 2020 Checklist and Flow Diagram for “Artificial Intelligence and Intelligent Tutoring Systems in Mathematics Education: A Bibliometric Analysis (2001–2025)”. DOI: https://doi.org/10.5281/zenodo.18169054 (Kuncoro et al., 2026) The bibliometric dataset and analysis files are available under the Creative Commons Attribution 4.0 International (CC-BY 4.0) license. The PRISMA checklist and flow diagram are available under a CC0 public domain dedication. This study is a bibliometric analysis based on secondary bibliographic data and does not involve clinical trials, animal experiments, observational human data, or qualitative interviews. Therefore, CONSORT, ARRIVE, STROBE, COREQ, and SRQR guidelines are not applicable. The study follows established best practices for bibliometric research and reporting transparency. 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Publisher Full Text Author details Author details 1 Mathematics Education, Universitas Pendidikan Indonesia, Bandung, West Java, 40154, Indonesia 2 Mathematics and Science Education, Universitas Sarjanawiyata Tamansiswa, Yogyakarta, Special Region of Yogyakarta, 55167, Indonesia 3 Science Education, Universitas Sebelas Maret, Surakarta, Central Java, 57126, Indonesia 2 Mathematics and Science Education, Universitas Sarjanawiyata Tamansiswa, Yogyakarta, Special Region of Yogyakarta, 55167, Indonesia 3 Science Education, Universitas Sebelas Maret, Surakarta, Central Java, 57126, Indonesia Krida Singgih Kuncoro Roles: Conceptualization, Methodology, Visualization, Writing – Original Draft Preparation Roles: Conceptualization, Methodology, Visualization, Writing – Original Draft Preparation Putri Fitriasari Roles: Conceptualization, Formal Analysis, Investigation Roles: Conceptualization, Formal Analysis, Investigation Sony Yunior Erlangga Roles: Conceptualization, Investigation, Writing – Review & Editing Roles: Conceptualization, Investigation, Writing – Review & Editing Marfi Ario Roles: Conceptualization, Formal Analysis, Validation Roles: Conceptualization, Formal Analysis, Validation Dwiani Listya Kartika Roles: Conceptualization, Investigation, Validation Roles: Conceptualization, Investigation, Validation Competing interests No competing interests were disclosed. Grant information Indonesian Education Scholarship, Doctoral Scholarship Program for Indonesian Lecturers, administered by the Center for Higher Education Funding and Assessment, Indonesian Endowment Fund for Education, and the Ministry of Higher Education, Science, and Technology of the Republic of Indonesia. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Copyright © 2026 Kuncoro KS et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. metrics | Views | Downloads | | |---|---|---| | F1000Research | - | - | | PubMed Central Data from PMC are received and updated monthly. | - | - | Citations CITE how to cite this article Kuncoro KS, Fitriasari P, Erlangga SY et al. Artificial Intelligence and Intelligent Tutoring Systems in Mathematics Education: A Bibliometric Analysis (2001–2025) [version 1; peer review: 3 approved with reservations]. F1000Research 2026, 15:107 (https://doi.org/10.12688/f1000research.176098.1) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. track receive updates on this article Track an article to receive email alerts on any updates to this article. Current Reviewer Status: ? Key to Reviewer Statuses VIEW HIDE ApprovedThe paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approvedFundamental flaws in the paper seriously undermine the findings and conclusions Version 1 VERSION 1 PUBLISHED 23 Jan 2026 Views 0 How to cite this report: Sacchet M. Reviewer Report For: Artificial Intelligence and Intelligent Tutoring Systems in Mathematics Education: A Bibliometric Analysis (2001–2025) [version 1; peer review: 3 approved with reservations]. F1000Research 2026, 15:107 (https://doi.org/10.5256/f1000research.194128.r456080) The direct URL for this report is: https://f1000research.com/articles/15-107/v1#referee-response-456080 https://f1000research.com/articles/15-107/v1#referee-response-456080 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Reviewer Report 24 Feb 2026 Approved with Reservations VIEWS 0 This manuscript presents a large-scale bibliometric and science-mapping analysis of Artificial Intelligence (AI) and Intelligent Tutoring Systems (ITS) in mathematics education using Scopus-indexed records (N = 237). The objectives are stated at the end of the introduction: document the ... Continue reading I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close The objectives are stated at the end of the introduction: document the ... Continue reading This manuscript presents a large-scale bibliometric and science-mapping analysis of Artificial Intelligence (AI) and Intelligent Tutoring Systems (ITS) in mathematics education using Scopus-indexed records (N = 237). The objectives are stated at the end of the introduction: document the historical development of AI and ITS research in mathematics education and offer strategic insights to inform future research agendas, instructional design, and educational policy in technology-enhanced mathematics learning. The study is timely, methodologically grounded in PRISMA-style reporting, and uses appropriate tools (Bibliometrix R-package and its Biblioshiny web interface) to analyze production trends, collaboration patterns, and thematic evolution. I enjoyed reading the paper. The work has strong potential. It is well written and academically sound. These figures are methodologically appropriate and aligned with standard bibliometric practice. Transparency and data availability is guaranteed by the deposited datasets in Zenodo. Limitations are clearly detailed. However, several issues require revision. This is a valuable study with strong mapping analyses and high relevance to mathematics education. However, critical issues must be corrected before it reaches full scientific rigor. The objectives are stated at the end of the introduction: document the historical development of AI and ITS research in mathematics education and offer strategic insights to inform future research agendas, instructional design, and educational policy in technology-enhanced mathematics learning. The study is timely, methodologically grounded in PRISMA-style reporting, and uses appropriate tools (Bibliometrix R-package and its Biblioshiny web interface) to analyze production trends, collaboration patterns, and thematic evolution. I enjoyed reading the paper. The work has strong potential. It is well written and academically sound. These figures are methodologically appropriate and aligned with standard bibliometric practice. Transparency and data availability is guaranteed by the deposited datasets in Zenodo. Limitations are clearly detailed. However, several issues require revision. - Timespan inconsistencies (2001–2025 vs. 2001–2026). 2026 has just started, I would suggest stopping in 2025 and skip work published in 2026. It was also used as an exclusion criterion in the Search Strategy: “Of these, one article published in 2026 was excluded as it fell outside the analysis range (2001–2025)”. This directly affects dataset definition, replicability, credibility, annual growth calculation, evolution interpretation. - Style of the introduction. The introduction reads like a narrative review before the bibliometric study (which is the core of the paper) and it includes conclusion-like statements before results. - Artificial intelligence is absent in the Query. Artificial Intelligence is in the title but it is not used in the query for Scopus search, it emerged afterwards as keywords in some of the papers. I suggest revising this inconsistency adding AI-related terms (e.g., “artificial intelligence,” “machine learning,” “deep learning”) to the query (this may change a lot the dataset) or justify why AI was omitted from the search string. - Clarify all selection criteria. The second step of the selection says “Documents irrelevant to mathematics education or not explicitly addressing AI/ITS were excluded.” It would be better to clarify decision rules, or provide borderline examples. In general, bibliometric parameters should be transparent. - Table 1. In the table, Annual Growth Rate % is 0 (zero). This looks inconsistent with the rest of the paper. This must be recalculated (if there was an error) or explained. - Research Questions. Research questions are stated only as “dimensions” at the end of the introduction, but they are mentioned as RQ in the headings of the discussion section. I would suggest adding explicit Research Questions. - Interpretative overreach. Several claims go beyond bibliometric evidence. For example, the summarized Conclusions “the findings highlight the critical role of teachers’ pedagogical readiness in ensuring that AI-enhanced mathematics education remains effective, inclusive, and sustainable.” present a sentence that it seems not directly derived from the findings of the paper. The same applies to the actionable implications. This is a valuable study with strong mapping analyses and high relevance to mathematics education. However, critical issues must be corrected before it reaches full scientific rigor. - Are the rationale for, and objectives of, the Systematic Review clearly stated? Yes - Are sufficient details of the methods and analysis provided to allow replication by others? Partly - Is the statistical analysis and its interpretation appropriate? Partly - Are the conclusions drawn adequately supported by the results presented in the review? Partly - If this is a Living Systematic Review, is the ‘living’ method appropriate and is the search schedule clearly defined and justified? (‘Living Systematic Review’ or a variation of this term should be included in the title.) No Competing Interests: No competing interests were disclosed. Reviewer Expertise: Mathematics Education, Digital Education CITE HOW TO CITE THIS REPORT Sacchet M. Reviewer Report For: Artificial Intelligence and Intelligent Tutoring Systems in Mathematics Education: A Bibliometric Analysis (2001–2025) [version 1; peer review: 3 approved with reservations]. F1000Research 2026, 15:107 (https://doi.org/10.5256/f1000research.194128.r456080) The direct URL for this report is: https://f1000research.com/articles/15-107/v1#referee-response-456080 https://f1000research.com/articles/15-107/v1#referee-response-456080 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. Views 0 How to cite this report: Kania N. Reviewer Report For: Artificial Intelligence and Intelligent Tutoring Systems in Mathematics Education: A Bibliometric Analysis (2001–2025) [version 1; peer review: 3 approved with reservations]. F1000Research 2026, 15:107 (https://doi.org/10.5256/f1000research.194128.r456084) The direct URL for this report is: https://f1000research.com/articles/15-107/v1#referee-response-456084 https://f1000research.com/articles/15-107/v1#referee-response-456084 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Reviewer Report 17 Feb 2026 Nia Kania, Universitas Majalengka,, Majalengka,, Majalengka,, Indonesia; Mathematics Education, Universitas Majalengka (Ringgold ID: 185698), Majalengka, West Java, Indonesia Approved with Reservations VIEWS 0 This manuscript presents a bibliometric/science-mapping analysis of publications on Artificial Intelligence and Intelligent Tutoring Systems in mathematics education, using Scopus-indexed records and analyses conducted with Bibliometrix/Biblioshiny. The study reports a marked increase in publications after 2019, a long-tail pattern of ... Continue reading I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close This manuscript presents a bibliometric/science-mapping analysis of publications on Artificial Intelligence and Intelligent Tutoring Systems in mathematics education, using Scopus-indexed records and analyses conducted with Bibliometrix/Biblioshiny. The study reports a marked increase in publications after 2019, a long-tail pattern of author productivity consistent with Lotka’s law, uneven international collaboration, and thematic structures/evolution derived from keyword co-occurrence, thematic mapping, thematic evolution analysis, and multiple correspondence analysis. The authors interpret these patterns as indicating a paradigm shift toward adaptive, data-driven learning systems and emphasize the importance of teachers’ pedagogical readiness for sustainable AI integration. Major comments I have no competing interests. Recommendation (approval status) Approved with Reservations Justification: The manuscript addresses a timely topic and uses appropriate bibliometric methods, yielding potentially useful insights into themes and collaborations. However, there are critical issues affecting scientific soundness and reproducibility, particularly inconsistent time spans and dataset definitions, and insufficient reporting of screening criteria and bibliometric parameter settings. Addressing these points and tightening the interpretation to align with what the bibliometric evidence supports would substantially strengthen the manuscript. Major comments - Critical inconsistency in scope/timespan and dataset definition - The title/abstract indicates 2001–2025, while the Results/figures/tables refer to 2001–2026 and include discussion of 2025–2026.Required revision: ensure complete alignment across (a) title, (b) Methods (PUBYEAR criteria and extraction date), (c) the reported dataset characteristics, (d) all figures/tables (annual production, thematic evolution, etc.), and (e) discussion/conclusions. Explicitly state the final dataset: N records, exact timespan, date of Scopus extraction. - Screening decisions and relevance criteria need operationalization - Current inclusion/exclusion descriptions (“irrelevant”, “not explicitly addressing AI/ITS”, “ambiguous records manually reviewed”) are not sufficiently operational for replication. - Required revision: provide explicit operational criteria, examples of borderline cases, and a clear reviewer adjudication process (e.g., independent screening, consensus, third reviewer). - Report bibliometric parameter settings and methodological choices in sufficient detail - For science mapping, specify thresholds and settings (e.g., keyword fields used, minimum occurrence thresholds, normalization method, clustering algorithm and parameters, thematic map configuration, MCA settings). - For keyword standardization, document the term-merging rules (beyond a few examples) and, if possible, share the thesaurus/cleaning dictionary used. - Align framing: “systematic review” versus “bibliometric analysis.” - The work is best positioned as a bibliometric/science-mapping study. While PRISMA-style flow reporting can improve transparency, the manuscript should avoid implying that it provides evidence synthesis of educational effectiveness or causal impact. Clarifying this will improve methodological coherence. - Temper claims and clarify inferential limits - Statements linking publication growth to LLM/generative AI and attributing late-year declines to indexing delays should be presented cautiously and/or supported with additional analyses (e.g., temporal keyword trend analyses for “LLM/ChatGPT/generative AI”). - Minor comments - Correct minor typos (e.g., “oreover” → “Moreover”). - Ensure consistent reporting of the study period throughout the manuscript, including captions and table headings. - Consider adding a concise limitations paragraph in the Methods highlighting single-database scope, English-only restriction, and document type filters. - If feasible, provide an appendix with a minimal list of included records (e.g., title, year, source) or clear instructions enabling readers with Scopus access to reproduce the record set precisely. I have no competing interests. Recommendation (approval status) Approved with Reservations Justification: The manuscript addresses a timely topic and uses appropriate bibliometric methods, yielding potentially useful insights into themes and collaborations. However, there are critical issues affecting scientific soundness and reproducibility, particularly inconsistent time spans and dataset definitions, and insufficient reporting of screening criteria and bibliometric parameter settings. Addressing these points and tightening the interpretation to align with what the bibliometric evidence supports would substantially strengthen the manuscript. - Are the rationale for, and objectives of, the Systematic Review clearly stated? Yes - Are sufficient details of the methods and analysis provided to allow replication by others? Partly - Is the statistical analysis and its interpretation appropriate? Partly - Are the conclusions drawn adequately supported by the results presented in the review? Partly - If this is a Living Systematic Review, is the ‘living’ method appropriate and is the search schedule clearly defined and justified? (‘Living Systematic Review’ or a variation of this term should be included in the title.) Not applicable Competing Interests: No competing interests were disclosed. Reviewer Expertise: Mathematics education CITE HOW TO CITE THIS REPORT Kania N. Reviewer Report For: Artificial Intelligence and Intelligent Tutoring Systems in Mathematics Education: A Bibliometric Analysis (2001–2025) [version 1; peer review: 3 approved with reservations]. F1000Research 2026, 15:107 (https://doi.org/10.5256/f1000research.194128.r456084) The direct URL for this report is: https://f1000research.com/articles/15-107/v1#referee-response-456084 https://f1000research.com/articles/15-107/v1#referee-response-456084 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. Views 0 How to cite this report: Opesemowo OAG. Reviewer Report For: Artificial Intelligence and Intelligent Tutoring Systems in Mathematics Education: A Bibliometric Analysis (2001–2025) [version 1; peer review: 3 approved with reservations]. F1000Research 2026, 15:107 (https://doi.org/10.5256/f1000research.194128.r456085) The direct URL for this report is: https://f1000research.com/articles/15-107/v1#referee-response-456085 https://f1000research.com/articles/15-107/v1#referee-response-456085 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Reviewer Report 16 Feb 2026 Approved with Reservations VIEWS 0 The article presents a well-structured bibliometric analysis of Artificial Intelligence (AI) and Intelligent Tutoring Systems (ITS) in mathematics education across a twenty-five-year period. The study is timely, methodologically grounded, and aligned with current scholarly interest in AI-driven learning systems. It ... Continue reading I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close The article presents a well-structured bibliometric analysis of Artificial Intelligence (AI) and Intelligent Tutoring Systems (ITS) in mathematics education across a twenty-five-year period. The study is timely, methodologically grounded, and aligned with current scholarly interest in AI-driven learning systems. It offers valuable mapping of publication trends, thematic evolution, and collaborative networks. However, several conceptual, methodological, and reporting issues require clarification and strengthening to enhance rigor, transparency, and scholarly contribution. Title and Positioning The title clearly communicates the study’s scope, method, and timeframe. It accurately signals that the work is a bibliometric systematic review. However, the combination of “systematic review” and “bibliometric analysis” could benefit from conceptual clarification. The article primarily performs bibliometric science mapping rather than a systematic synthesis of research findings. The authors should explicitly distinguish bibliometric review from evidence synthesis to avoid conceptual ambiguity.

The abstract is well organized and follows a structured format. The background clearly identifies the need for domain-specific bibliometric mapping. The methods succinctly describe database selection, analytical tools, and inclusion criteria. The results section effectively summarizes major trends such as post-2019 growth, long-tail authorship distribution, and emerging themes. However, the abstract would benefit from:

The introduction is comprehensive and demonstrates strong theoretical grounding. It successfully situates AI and ITS within broader educational transformation and provides relevant scholarly references. Strengths include: Research Gap and Objectives The article identifies that previous bibliometric studies often examine AI in education broadly rather than mathematics education specifically. This is a valid and well-articulated gap. However, the study’s specific research questions are not explicitly stated in interrogative form. The four analytical dimensions are described, but they function more as analytical components than clearly framed research questions. Explicit research questions would strengthen conceptual clarity.

The methods section is one of the strongest parts of the article. It is transparent, systematic, and reproducible. Strengths

The results section is detailed, systematic, and visually supported. The descriptive bibliometric reporting is comprehensive. Main Information Table The dataset description is clear and informative. However, the reported annual growth rate of zero percent appears inconsistent with the observed increase in publications. This requires explanation or recalculation. Scientific Production The identification of post-2019 growth is convincing and logically interpreted. The discussion linking growth to AI expansion is plausible but somewhat speculative without citation-based verification. Authorship and Collaboration The long-tail distribution and Lotka’s Law interpretation are appropriate. The collaboration analysis is informative but could include network centrality metrics or collaboration strength indicators. Country Contributions The dominance of the United States and China is clearly presented. However, the authors should interpret structural causes (research funding, infrastructure, publication access). Thematic Analyses The keyword networks, thematic maps, and evolution diagrams are methodologically sound and well interpreted. The identification of pedagogical versus technological clusters is especially valuable. One limitation is that interpretation sometimes exceeds what keyword frequency alone can support. For example, claims about paradigm shifts or pedagogical transformation should be cautiously framed.

The discussion integrates results with broader AI-education trends. It appropriately interprets growth stages and interdisciplinary character. Strengths include: The conclusion summarizes major findings effectively and emphasizes pedagogical readiness. This is a meaningful insight but not strongly demonstrated through bibliometric evidence. The authors should frame this as an interpretive implication rather than an empirical result. Strengths of the Study Recommendations for Improvement The article makes a meaningful and timely contribution to understanding the evolution of AI and ITS research in mathematics education. Its methodological execution is strong, and its mapping results are valuable for scholars and policymakers. With revisions addressing conceptual clarity, methodological transparency, and interpretive restraint, the manuscript has strong potential for indexing and impact. Title and Positioning The title clearly communicates the study’s scope, method, and timeframe. It accurately signals that the work is a bibliometric systematic review. However, the combination of “systematic review” and “bibliometric analysis” could benefit from conceptual clarification. The article primarily performs bibliometric science mapping rather than a systematic synthesis of research findings. The authors should explicitly distinguish bibliometric review from evidence synthesis to avoid conceptual ambiguity.

The abstract is well organized and follows a structured format. The background clearly identifies the need for domain-specific bibliometric mapping. The methods succinctly describe database selection, analytical tools, and inclusion criteria. The results section effectively summarizes major trends such as post-2019 growth, long-tail authorship distribution, and emerging themes. However, the abstract would benefit from: - Explicit reporting of the search date and version of database extraction. - Clearer articulation of the research gap addressed. - Quantitative indicators of major findings (e.g., percentage growth, leading country share).

The introduction is comprehensive and demonstrates strong theoretical grounding. It successfully situates AI and ITS within broader educational transformation and provides relevant scholarly references. Strengths include: - Clear explanation of personalization and adaptive learning. - Historical positioning of cognitive tutors. - Logical narrowing from general AI in education to mathematics-specific applications. - Conceptual density The introduction is overly expansive and occasionally descriptive rather than analytical. It reads partly like a narrative review rather than framing a bibliometric investigation. - Insufficient justification for bibliometric method Although the authors note gaps in existing reviews, they do not clearly specify what unique insight bibliometric mapping offers beyond prior reviews. - Limited critical stance The introduction largely promotes AI and ITS benefits but provides limited discussion of controversies, methodological challenges, or critical perspectives. - Minor language issues There is at least one typographical error (“oreover”), and several sentences are overly long and syntactically dense. Research Gap and Objectives The article identifies that previous bibliometric studies often examine AI in education broadly rather than mathematics education specifically. This is a valid and well-articulated gap. However, the study’s specific research questions are not explicitly stated in interrogative form. The four analytical dimensions are described, but they function more as analytical components than clearly framed research questions. Explicit research questions would strengthen conceptual clarity.

The methods section is one of the strongest parts of the article. It is transparent, systematic, and reproducible. Strengths - Use of PRISMA reporting structure. - Clearly documented search string. - Explicit inclusion and exclusion criteria. - Data cleaning procedures explained. - Reliability supported through independent checking. - Open data availability. - Single database limitation The exclusive use of Scopus is justified by coverage, but this introduces selection bias. The authors acknowledge coverage strength but do not sufficiently discuss exclusion effects (e.g., Web of Science, ERIC). - Language restriction bias English-only inclusion should be discussed as a potential distortion of global research representation. - Search query scope The search string may omit key AI terminology (e.g., machine learning, deep learning, neural networks, learning analytics). This could under-represent relevant publications. - Time span inconsistency The stated analysis period is 2001–2025, but some tables reference 2001–2026. This inconsistency must be corrected. - Screening transparency The manual review process is mentioned but not described in procedural detail (e.g., reviewer agreement, decision rules).

The results section is detailed, systematic, and visually supported. The descriptive bibliometric reporting is comprehensive. Main Information Table The dataset description is clear and informative. However, the reported annual growth rate of zero percent appears inconsistent with the observed increase in publications. This requires explanation or recalculation. Scientific Production The identification of post-2019 growth is convincing and logically interpreted. The discussion linking growth to AI expansion is plausible but somewhat speculative without citation-based verification. Authorship and Collaboration The long-tail distribution and Lotka’s Law interpretation are appropriate. The collaboration analysis is informative but could include network centrality metrics or collaboration strength indicators. Country Contributions The dominance of the United States and China is clearly presented. However, the authors should interpret structural causes (research funding, infrastructure, publication access). Thematic Analyses The keyword networks, thematic maps, and evolution diagrams are methodologically sound and well interpreted. The identification of pedagogical versus technological clusters is especially valuable. One limitation is that interpretation sometimes exceeds what keyword frequency alone can support. For example, claims about paradigm shifts or pedagogical transformation should be cautiously framed.

The discussion integrates results with broader AI-education trends. It appropriately interprets growth stages and interdisciplinary character. Strengths include: - Linking findings to Price’s Law. - Interpreting trend topics historically. - Recognizing increasing teacher-related research. - Interpretive overreach Some claims about effectiveness of AI systems are not derived from bibliometric data but from external literature. - Limited critical analysis Ethical concerns, digital divide, and implementation barriers are mentioned but not systematically linked to findings. - Insufficient comparison with prior bibliometric studies The discussion would benefit from direct comparison with earlier AI-in-education mapping studies. The conclusion summarizes major findings effectively and emphasizes pedagogical readiness. This is a meaningful insight but not strongly demonstrated through bibliometric evidence. The authors should frame this as an interpretive implication rather than an empirical result. Strengths of the Study - Clear methodological structure. - Transparent search and screening process. - Rich science-mapping analyses. - Longitudinal coverage across twenty-five years. - Open data availability. - Strong visualization and thematic modeling. Recommendations for Improvement - Clarify conceptual distinction between bibliometric review and systematic review. - Correct timeframe inconsistencies. - Expand methodological justification for database selection. - Provide explicit research questions. - Reduce interpretive claims not directly supported by bibliometric evidence. - Strengthen critical discussion of structural and ethical dimensions. - Improve reporting transparency for manual screening. The article makes a meaningful and timely contribution to understanding the evolution of AI and ITS research in mathematics education. Its methodological execution is strong, and its mapping results are valuable for scholars and policymakers. With revisions addressing conceptual clarity, methodological transparency, and interpretive restraint, the manuscript has strong potential for indexing and impact. - Are the rationale for, and objectives of, the Systematic Review clearly stated? Yes - Are sufficient details of the methods and analysis provided to allow replication by others? Partly - Is the statistical analysis and its interpretation appropriate? Yes - Are the conclusions drawn adequately supported by the results presented in the review? Yes - If this is a Living Systematic Review, is the ‘living’ method appropriate and is the search schedule clearly defined and justified? (‘Living Systematic Review’ or a variation of this term should be included in the title.) Yes Competing Interests: No competing interests were disclosed. Reviewer Expertise: Educational assessment, AI in Education, and Psychometric analysis. CITE HOW TO CITE THIS REPORT Opesemowo OAG. Reviewer Report For: Artificial Intelligence and Intelligent Tutoring Systems in Mathematics Education: A Bibliometric Analysis (2001–2025) [version 1; peer review: 3 approved with reservations]. F1000Research 2026, 15:107 (https://doi.org/10.5256/f1000research.194128.r456085) The direct URL for this report is: https://f1000research.com/articles/15-107/v1#referee-response-456085 https://f1000research.com/articles/15-107/v1#referee-response-456085 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. Alongside their report, reviewers assign a status to the article: - Approved - Approved with reservations - Not approved | Invited Reviewers | ||| |---|---|---|---| | 1 | 2 | 3 | | | Version 1 23 Jan 26 | read | read | read | Sign up for content alerts You are now signed up to receive this alert Alongside their report, reviewers assign a status to the article: Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved - fundamental flaws in the paper seriously undermine the findings and conclusions Provide sufficient details of any financial or non-financial competing interests to enable users to assess whether your comments might lead a reasonable person to question your impartiality. 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