Aug 2026· International journal of technology in education and science· 0 citations
TL;DR
A comprehensive bibliometric analysis of research at the intersection of AI-focused TPD and classroom pedagogy identifies three dominant research clusters, which highlight a growing shift from technical adoption toward research exploring teachers’ readiness, ethical awareness, and pedagogical innovation when integrating AI.
Abstract
The integration of Artificial Intelligence (AI) into education is rapidly transforming teaching methods and educators’ professional roles. However, while the literature on AI in education has expanded significantly, prior bibliometric studies have primarily focused on broad technological perspectives, higher-education contexts, or student-centred learning. Moreover, limited attention has been devoted to how AI specifically shapes Teacher Professional Development (TPD) and its contribution to enhanced teaching practices. Thus, this study addresses this gap by conducting a comprehensive bibliometric analysis of research at the intersection of AI-focused TPD and classroom pedagogy. Accordingly, a total of 110 peer-reviewed publications (2018-2025) were analysed using descriptive bibliometrics, citation performance indicators, and science-mapping techniques, including co-authorship and keyword co-occurrence networks. Additionally, findings reveal a sharp acceleration in scholarly interest from 2022 onwards, indicating a rapidly maturing field driven by global educational digitalisation. The United States (US), Hong Kong, and Saudi Arabia emerge as leading contributors, while journals such as Education Sciences and Computers and Education: Artificial Intelligence serve as influential publication outlets. Specifically, thematic mapping identifies three dominant research clusters: (1) AI-enhanced teacher learning and competencies, (2) Generative AI (GenAI) applications in professional practice, and (3) pedagogical transformation through data-driven and intelligent systems. Overall, this study highlights a growing shift from technical adoption toward research exploring teachers’ readiness, ethical awareness, and pedagogical innovation when integrating AI. Likewise, the findings provide a valuable evidence base to guide future TPD initiatives aimed at strengthening AI-aligned teacher capabilities and improving instructional quality.
The rapid emergence of generative artificial intelligence (GenAI), particularly large language models, has significantly transformed educational research and practice, resulting in a rapidly expanding yet fragmented body of literature. This study aims to systematically map the research landscape of GenAI in education and examine its implications for teacher education through a comprehensive bibliometric analysis. Using the Scopus database, 16,250 documents published from 2021 to 2025 were analyzed through performance analysis and science mapping techniques, including keyword co-occurrence, co-authorship network analysis, and citation analysis to capture the structural and intellectual development of the field. The findings reveal a sharp increase in publication output beginning in 2023, indicating a transition from an emergent to a high-growth phase of research development. Research productivity and scholarly influence are concentrated among a limited group of authors, institutions, and countries, particularly the United States and China, reflecting uneven global participation. Collaboration networks exhibit a multi-clustered structure, suggesting active yet fragmented research communities with limited cross-group integration. Citation patterns indicate that a small number of recent publications exert strong influence and serve as foundational references in the field. Thematic analysis further identifies three dominant and interconnected areas: core technological developments, educational applications, and domain-specific implementations, highlighting the interdisciplinary nature of GenAI in education. The field is expanding rapidly but remains structurally fragmented and is still undergoing intellectual consolidation. These findings emphasize the need for more cohesive research efforts, stronger international collaboration, and greater integration across thematic domains. Implications for teacher education include the integration of AI-related competencies into pre-service programs, the adoption of pedagogically grounded and context-sensitive approaches, and the provision of continuous professional development to support teachers in AI-enhanced learning environments.
Jefry E. Aransado, Jeson V. Viñas, Joshua M. Ayade et al.· Multidisciplinary Reviews· 0 citations
Artificial intelligence (AI) has increasingly gained attention in primary education due to its potential to support personalized learning, adaptive assessment, and instructional decision-making. Despite the rapid growth of publications, research in this field remains fragmented, with limited understanding of its intellectual structure and thematic evolution. This study aims to systematically map the development, research trends, and thematic trajectories of AI research in primary education through a bibliometric approach. A total of 547 peer-reviewed journal articles indexed in the Scopus database from 2018 to 2025 were analyzed using performance analysis and science mapping techniques supported by VOSviewer. The findings reveal a significant increase in publication output, indicating the consolidation of AI in primary education as a distinct research domain. Keyword co-occurrence and cluster analysis identify three major thematic clusters: (1) educational technology and teacher capacity building, (2) AI-driven instructional innovation and immersive learning, and (3) humanistic and developmental perspectives in primary education. Thematic evolution analysis demonstrates a clear shift from general digital technology integration (2018–2020), to institutional adaptation and teacher readiness (2021–2022), and finally toward an AI-centered and application-oriented research focus emphasizing personalized learning and assessment (2023–2025). However, ethical considerations, child-centered impacts, and long-term developmental outcomes remain underrepresented. This study provides a comprehensive overview of the intellectual landscape of AI research in primary education and offers strategic insights for future research agendas, emphasizing the need for ethically grounded, pedagogically informed, and developmentally appropriate AI implementation.
Artificial intelligence (AI) has emerged as a transformative force in higher education, especially with ChatGPT. Despite growing interest, there is a scarcity of bibliometric studies that systematically map the knowledge domain. Technological innovations present challenges and opportunities, but understanding does not keep pace with innovations. Higher education is at the center of the debate about AI impacts and necessary responses for effective integration. There are no studies that map the intellectual structure through co-citation nor emerging frontiers via bibliographic coupling about AI in higher education. Objective: to map the emergence, evolution and knowledge frontiers about artificial intelligence in higher education through robust bibliometric analyses, identifying consolidated theoretical clusters, emerging trends and future research opportunities for theoretical-practical advancement of the field. The field is structured on consolidated theoretical pillars, evolving towards an integrative multidisciplinary approach. AI offers significant transformative potential, but requires responsible implementation considering technological, pedagogical and ethical aspects. Systematic research is essential to maximize benefits while minimizing risks, promoting effective and equitable AI integration in higher education through robust scientific evidence. First systematization of the field's intellectual structure via co-citation and emerging frontiers via bibliographic coupling. Operational framework offers practical guide for institutional implementation. Research agenda directs future investigations. Contributes theoretically by identifying gaps and taxonomies, and practically by providing guidelines for educational managers on AI implementation policies, procedures and strategies based on scientific evidence.
A. A. de Lima, C. K. Yamaguchi, M. D. de Oliveira et al.· Revista edUCA - Revista Mult...· 1 citation
Objectives. As artificial intelligence increasingly enters educational practice, PE pedagogy faces a distinct question that extends beyond online, blended, or technology-enhanced delivery. This study mapped research on artificial intelligence in PE pedagogy from 2015 to 2025, focusing on publication growth, conceptual structure, interdisciplinary foundations, pedagogical applications, and emerging concerns.
Materials and Methods. Using a bibliometric and thematic review design, bibliographic data were retrieved from Scopus and analyzed through Bibliometrix/Biblioshiny and VOSviewer. After filtering and data cleaning, the final corpus comprised 1,000 documents published across 389 sources.
Results. The results revealed a marked post-2020 expansion, indicating growing scholarly attention to AI-related pedagogical applications in PE. The field was strongly interdisciplinary, spanning PE, educational technology, computer science, engineering, and health-related domains. Conceptual and thematic analyses showed that the literature is anchored in PE, artificial intelligence, machine learning, deep learning, data mining, physical education teaching, virtual reality, and movement-related analytics. Bibliographic coupling and co-citation analyses further indicated that the field is developing through the convergence of computational innovation and pedagogical application rather than within a single disciplinary tradition.
Conclusions. The study positions AI in PE pedagogy as an emerging computational-pedagogical research domain. The evidence should be interpreted as structural and thematic mapping, not as proof of instructional effectiveness. As a conceptual contribution, the review advances a Pedagogically Accountable AI in PE Framework centered on computational function, pedagogical translation, embodied movement learning, human agency, and ethical accountability.
Keywords: artificial intelligence, bibliometric analysis, emerging concerns, pedagogical applications, physical education.
J. Lobo· Physical Education Theory an...· 0 citations
Insightful insights are provided into publication trends, contributors, research themes, and emerging AI technologies in adaptive learning, which could assist researchers, educators, policymakers, and educational technology developers in improving intelligent adaptive learning systems.
Noor Fadzilah, Ab Rahman, Nurkaliza Khalid· e-Jurnal Penyelidikan dan In...· 0 citations
A bibliometric analysis of recent literature on the integration of AI into active b-learning methodologies in the university context suggests that the convergence between AI and active learning represents a promising path for pedagogical innovation in Higher Education.
Sergio Sargo Lopes, M. Lousã, Jorge Azevedo Simões et al.· Revista EDaPECI· 0 citations
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