Generative artificial intelligence in education from 2021 to 2025: a scientometric review
This study presents a bibliometric analysis of 965 peer-reviewed articles on generative artificial intelligence (GenAI) in education published from 2021 to 2025 in the Web of Science Core Collection. Through keyword co-occurrence, co-citation, and collaboration network analyses, it identifies core r...
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| Language: | en |
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Sciedu Press
2025
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| Online Access: | http://psasir.upm.edu.my/id/eprint/123713/1/123713.pdf http://psasir.upm.edu.my/id/eprint/123713/ https://www.sciedupress.com/journal/index.php/jct/article/view/28729 |
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| author | Guo, Junmin Mohd Sufian Kang, Enio Kang Ghazali, Norliza |
| author_facet | Guo, Junmin Mohd Sufian Kang, Enio Kang Ghazali, Norliza |
| author_sort | Guo, Junmin |
| building | UPM Library |
| collection | Institutional Repository |
| content_provider | Universiti Putra Malaysia |
| content_source | UPM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | This study presents a bibliometric analysis of 965 peer-reviewed articles on generative artificial intelligence (GenAI) in education published from 2021 to 2025 in the Web of Science Core Collection. Through keyword co-occurrence, co-citation, and collaboration network analyses, it identifies core research themes, intellectual structures, and developmental trends. Findings reveal an exponential rise in GenAI-related publications, with dominant themes centred on technological applications of GenAI in teaching and assessment—especially ChatGPT—alongside technology acceptance mechanisms and learner outcomes such as motivation and self-efficacy. Three major thematic clusters emerge: GenAI educational applications, user adoption theories, and learning impacts. Co-citation patterns show strong reliance on traditional acceptance models like TAM, indicating limited development of GenAI-specific theoretical frameworks. Collaboration analyses reveal fragmented author networks and uneven global participation, concentrated mainly in North America and East Asia. The study highlights research gaps, including ethical governance, creativity development, interdisciplinary applications, and insufficient qualitative or mixed-method studies. It recommends developing theoretical models tailored to GenAI’s interactive and multimodal characteristics, strengthening ethical and cross-cultural frameworks, expanding interdisciplinary innovation, and enhancing global research cooperation to support the sustainable and responsible integration of GenAI in education. |
| format | Article |
| id | my.upm.eprints-123713 |
| institution | Universiti Putra Malaysia |
| language | en |
| publishDate | 2025 |
| publisher | Sciedu Press |
| record_format | eprints |
| spelling | my.upm.eprints-1237132026-03-17T05:42:53Z http://psasir.upm.edu.my/id/eprint/123713/ Generative artificial intelligence in education from 2021 to 2025: a scientometric review Guo, Junmin Mohd Sufian Kang, Enio Kang Ghazali, Norliza This study presents a bibliometric analysis of 965 peer-reviewed articles on generative artificial intelligence (GenAI) in education published from 2021 to 2025 in the Web of Science Core Collection. Through keyword co-occurrence, co-citation, and collaboration network analyses, it identifies core research themes, intellectual structures, and developmental trends. Findings reveal an exponential rise in GenAI-related publications, with dominant themes centred on technological applications of GenAI in teaching and assessment—especially ChatGPT—alongside technology acceptance mechanisms and learner outcomes such as motivation and self-efficacy. Three major thematic clusters emerge: GenAI educational applications, user adoption theories, and learning impacts. Co-citation patterns show strong reliance on traditional acceptance models like TAM, indicating limited development of GenAI-specific theoretical frameworks. Collaboration analyses reveal fragmented author networks and uneven global participation, concentrated mainly in North America and East Asia. The study highlights research gaps, including ethical governance, creativity development, interdisciplinary applications, and insufficient qualitative or mixed-method studies. It recommends developing theoretical models tailored to GenAI’s interactive and multimodal characteristics, strengthening ethical and cross-cultural frameworks, expanding interdisciplinary innovation, and enhancing global research cooperation to support the sustainable and responsible integration of GenAI in education. Sciedu Press 2025-12-31 Article PeerReviewed text en cc_by_4 http://psasir.upm.edu.my/id/eprint/123713/1/123713.pdf Guo, Junmin and Mohd Sufian Kang, Enio Kang and Ghazali, Norliza (2025) Generative artificial intelligence in education from 2021 to 2025: a scientometric review. Journal of Curriculum and Teaching, 15 (1). pp. 102-116. ISSN 1927-2677; eISSN: 1927-2685 https://www.sciedupress.com/journal/index.php/jct/article/view/28729 Education 10.5430/jct.v15n1p102 |
| spellingShingle | Education Guo, Junmin Mohd Sufian Kang, Enio Kang Ghazali, Norliza Generative artificial intelligence in education from 2021 to 2025: a scientometric review |
| title | Generative artificial intelligence in education from 2021 to 2025: a scientometric review |
| title_full | Generative artificial intelligence in education from 2021 to 2025: a scientometric review |
| title_fullStr | Generative artificial intelligence in education from 2021 to 2025: a scientometric review |
| title_full_unstemmed | Generative artificial intelligence in education from 2021 to 2025: a scientometric review |
| title_short | Generative artificial intelligence in education from 2021 to 2025: a scientometric review |
| title_sort | generative artificial intelligence in education from 2021 to 2025: a scientometric review |
| topic | Education |
| url | http://psasir.upm.edu.my/id/eprint/123713/1/123713.pdf http://psasir.upm.edu.my/id/eprint/123713/ https://www.sciedupress.com/journal/index.php/jct/article/view/28729 |
| url_provider | http://psasir.upm.edu.my/ |