Jul 2026· West Science Interdisciplinary Studies· Vol 4, pp. 1205-1215· 0 citations· 21 references
TL;DR
A bibliometric analysis of scientific publications indexed in the Scopus database provides a comprehensive understanding of the evolution, current trends, and future directions of Generative AI for Learning research, emphasizing the importance of interdisciplinary collaboration and responsible AI implementation to support sustainable educational innovation.
Abstract
The rapid advancement of Generative Artificial Intelligence (GenAI) has significantly transformed educational practices by introducing new possibilities for personalized learning, intelligent tutoring systems, automated feedback, and AI-supported knowledge creation. This study aims to explore the global research landscape of Generative AI for Learning through a bibliometric analysis of scientific publications indexed in the Scopus database. A comprehensive literature search was conducted to identify relevant publications, followed by performance analysis and science mapping using VOSviewer. The analysis examined publication trends, highly cited literature, keyword co-occurrence, citation networks, author collaboration, institutional contributions, and international research patterns. The findings reveal that research on generative AI in learning has experienced substantial growth, particularly following the emergence of ChatGPT and large language models. The intellectual structure of the field is dominated by three interconnected themes: technological advancement of artificial intelligence, educational integration of AI-based learning systems, and human-centered considerations including AI literacy, critical thinking, ethics, and responsible adoption. Influential publications highlight both the opportunities and challenges of generative AI, including improvements in learning effectiveness, academic transformation, assessment challenges, and potential cognitive impacts. Furthermore, collaboration analysis indicates that the United States plays a central role in global research networks, while contributions from countries across Asia, Europe, and other regions continue to expand. This study provides a comprehensive understanding of the evolution, current trends, and future directions of Generative AI for Learning research, emphasizing the importance of interdisciplinary collaboration and responsible AI implementation to support sustainable educational innovation.
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
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.
Bilal Alhawamdeh, Mohd Fadzil Abdul Hanid, N. D. Abd Halim et al.· International journal of tec...· 0 citations
The rapid emergence of Generative Artificial Intelligence (GenAI) has transformed higher education by reshaping teaching, learning, assessment, and institutional practices. Despite the rapid growth of scholarly publications, existing bibliometric studies have largely focused on descriptive indicators, providing a limited understanding of the field's intellectual foundations, conceptual evolution, and theoretical development. This study addresses this gap by conducting a comprehensive bibliometric and science mapping analysis of 3,214 Scopus-indexed publications on Generative AI in higher education published between 2020 and 2026. Following the PRISMA 2020 framework, bibliographic data were analyzed using VOSviewer version 1.6.20 to examine publication trends, co-citation networks, and keyword co-occurrence patterns. The findings reveal exponential growth in research following the widespread adoption of large language models, with publication output expanding rapidly since 2023. Co-citation analysis identified five major intellectual domains encompassing AI-assisted learning applications, student engagement, methodological and theoretical foundations, AI-supported language learning, and technology acceptance. Co-word analysis further revealed four dominant conceptual themes: pedagogical integration, learner-centered research, computational technologies, and ethical governance. Collectively, these findings demonstrate that Generative AI research has evolved from technologyoriented investigations to a multidisciplinary educational ecosystem that emphasizes pedagogical innovation, institutional transformation, and responsible AI implementation. Building upon these empirical findings, the study proposes the AI Pedagogical Knowledge Ecosystem Framework, which integrates technological innovation, pedagogical transformation, learner engagement, institutional governance, and educational outcomes into a unified conceptual model. By combining co-citation and co-word analyses within a single science mapping framework, this study extends previous bibliometric research beyond descriptive mapping toward theory-informed conceptual development and knowledge synthesis. The findings provide valuable guidance for educators, higher education institutions, policymakers, instructional designers, and researchers seeking to support the responsible and sustainable integration of Generative AI in higher education.
M. A. P. Galang· International Journal of Edu...· 0 citations
Generative artificial intelligence (GenAI) has reshaped teaching, learning and assessment in higher education faster than the literature describing it has been consolidated. Existing bibliometric reviews of this area are typically built on a single database, organised around ChatGPT alone, or extended to education at all levels and they rarely report the analytical parameters required for replication. This study addresses that gap by mapping the scientific production structure, intellectual and conceptual organisation and thematic evolution of GenAI research in higher education on a dual-database (Web of Science Core Collection and Scopus), PRISMA 2020-screened and fully parameter-transparent corpus of 2,992 articles and reviews published between 2022 and 2025, drawn from 728 sources and 9,948 authors. Descriptive, keyword co-occurrence, correspondence-analysis-based conceptual structure, strategic (thematic) map and thematic evolution analyses were triangulated across Bibliometrix/Biblioshiny and VOSviewer. Output grew from 5 documents in 2022 to 2,001 in 2025 a 400-fold expansion, with 66.9% of the entire corpus published in the final year alone at a mean of 18.46 citations per document. Impact is steeply concentrated: the ten most-cited documents (0.33% of the corpus) account for 8.75% of all citations. Production is led by China (n = 890), the United States (n = 617) and Australia (n = 401), yet international co-authorship stands at only 10.11%, indicating a field that is collaborative but overwhelmingly domestic. Türkiye (n = 96) ranks in the same output band as the United Arab Emirates, Peru and Hong Kong, but below regional peers such as Saudi Arabia, Indonesia and Jordan. Two independent clustering procedures each return a four-cluster architecture, but not the same four: the co-occurrence network built on indexed vocabulary separates the field by disciplinary setting instructional technology, health-professions education, technology acceptance and a general artificial intelligence core whereas correspondence analysis on author keywords separates it by conceptual concern into technological, educational-application, ethical-assessment and pedagogical dimensions. Generative AI is the single motor theme and the field shifts clearly from technological exploration towards pedagogical integration and academic integrity. Beyond describing the field, the study contributes a fully reproducible bibliometric protocol and a critical reading of the field’s ChatGPT-centrism, its collaboration deficit and its early signs of thematic saturation.
Şener Balat· Black Sea Journal of Enginee...· 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 was conducted to examine publication trends, disciplinary distributions, international collaborations, and their alignment with the United Nations Sustainable Development Goals (SDGs) to indicate a rapid acceleration of research output post-2019.
Konstantinos Karampelas· International Journal of Edu...· 0 citations
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