Generative AI and educational innovation: Bibliometric analysis and science mapping
Article Sidebar
Main Article Content
Abstract
This research analyses the literary-scientific intersection between GenAI (generative AI) and educational innovation. To this end, bibliometric and scientific mapping techniques are employed. The context stems from the normalisation of the use of ChatGPT, a generative AI tool launched in November 2022. The study has three objectives: firstly, to characterise the temporal evolution of scientific output relating to GenAI and educational innovation; secondly, to identify thematic networks and their dynamics; and thirdly, to map international collaboration networks in order to highlight their cohesion and/or maturity. Methodology: The study is based on a document selection from WoS (Web of Science), carried out via a topic search (TS), which combines terms related to GenAI and educational innovation, without any initial restrictions. Given the novelty of this subject area, two sub-periods of analysis are defined: P1 (2023–2024) and P2 (2025–first half of 2026). PRISMA and the SALSA framework are used to structure, refine and standardise the terminology. Furthermore, SciMAT is used for scientific mapping analysis, and VOSviewer for the co-occurrence of keywords and co-authorship by country. Results: The final corpus comprises 303 documents, with 74.6% concentrated in P2. Publication output increased between 2023 and 2025; the 92 documents recorded for 2026 cover only the first half of the year and are not directly comparable with the preceding annual totals. During the period analysed, journal articles (64.7%) and conference papers (27.1%) predominate. Nine clusters are identified, comprising a central core and eight peripheral clusters grouped into four thematic domains: technological, methodological, competence-related, and emerging practices. The longitudinal analysis indicates thematic expansion, from two themes in P1 to five in P2. Of the 44 countries meeting the publication threshold, 38 form the main connected component, organised into four communities with differences in connectivity and institutional concentration. Discussion: The findings describe an emerging field whose retrieved scientific output follows the public launch of ChatGPT. They also suggest a broadening of research interest from the tools and their immediate implications towards their curricular, methodological, and competence-related integration. Conclusions: The study provides an empirical characterization of a rapidly expanding and maturing field, with a stable core and a diversified periphery. Limitations include the fact that the results depend on the scope of Web of Science, and the citation window for P2 (as well as the novelty of the subject matter) is structurally short. In future research, it is suggested that the analysis be triangulated with Scopus and regional databases, and that the evolution of author fragmentation be examined to ascertain whether denser collaborative structures have emerged.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
The Journal of Communication of SEECI recognizes and promotes copyright rights, as well as the need to disseminate knowledge in an accessible and equitable manner. Our journal operates under a Creative Commons License CC BY-NC 4.0, which allows authors and users to:
- Share and Adapt: Copy, redistribute, and adapt the material published in the journal in any medium or format.
- Attribution: Properly acknowledge authorship and provide a link to the license, indicating if any changes have been made.
- Non-Commercial: Do not use the material for commercial purposes without the express permission of the authors and the journal.
- Authors retain copyright and may enter into non-exclusive agreements for self-archiving, deposit, or distribution of the publisher's version published in this journal, including institutional, national, or international repositories, and personal websites.
References
Chan, C. y Hu, W. (2023). Students’ voices on generative AI: Perceptions, benefits, and challenges in higher education. International Journal of Educational Technology in Higher Education, 20, article 43. https://doi.org/10.1186/s41239-023-00411-8.
Cobo, M. J., López-Herrera, A. G., Herrera-Viedma, E. y Herrera, F. (2012). SciMAT: A new science mapping analysis software tool. Journal of the American Society for Information Science and Technology, 63(8), 1609–1630. https://doi.org/10.1002/asi.22688
Donthu, N., Kumar, S., Mukherjee, D., Pandey, N. y Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285–296. https://doi.org/10.1016/j.jbusres.2021.04.070
Gorbatov, S., Khapova, S. y Lysova, E. (2018). Personal branding: Interdisciplinary systematic Review and Research Agenda. Frontiers in Psychology, 9. https://doi.org/10.3389/fpsyg.2018.02238
Grant, M. J. y Booth, A. (2009). A typology of reviews: An analysis of 14 review types and associated methodologies. Health Information and Libraries Journal, 26(2), 91–108. https://doi.org/10.1111/j.1471-1842.2009.00848.x
Labrecque, L. I., Markos, E. y Milne, G. R. (2011). Online personal branding: Processes, challenges, and implications. Journal of Interactive Marketing, 25(1), 37–50. https://doi.org/10.1016/j.intmar.2010.09.002
Miao, F. y Holmes, W. (2023). Guidance for generative AI in education and research. UNESCO. https://doi.org/10.54675/EWZM9535
Moher, D., Liberati, A., Tetzlaff, J. y Altman, D. G. (2009). Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. PLoS Medicine, 6(7), e1000097. https://doi.org/10.1371/journal.pmed.1000097
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., … Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372(71). https://doi.org/10.1136/bmj.n71
Sánchez-Serrano, S., Pedraza-Navarro, I. y Donoso-González, M. (2022). ¿Cómo hacer una revisión sistemática siguiendo el protocolo PRISMA? Usos y estrategias fundamentales para su aplicación en el ámbito educativo a través de un caso práctico. Bordón. Revista de Pedagogía, 74(3), 51-66. https://doi.org/10.13042/Bordon.2022.95090
Serdyukov, P. (2017). Innovation in education: What works, what doesn’t, and what to do about it? Journal of Research in Innovative Teaching & Learning, 10(1), 4–33. https://doi.org/10.1108/JRIT-10-2016-0007
van Eck, N. J. y Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84, 523–538. https://doi.org/10.1007/s11192-009-0146-3
Yan, L., Greiff, S., Teuber, Z. y Gašević, D. (2024). Promises and challenges of generative artificial intelligence for human learning. Nature Human Behaviour, 8, 1839–1850. https://doi.org/10.1038/s41562-024-02004-5
Yusuf, A., Pervin, N. y Román-González, M. (2024). Generative AI and the future of higher education: A threat to academic integrity or reformation? Evidence from multicultural perspectives. International Journal of Educational Technology in Higher Education, 21, 1–29. https://doi.org/10.1186/s41239-024-00453-6
Zupic, I. y Čater, T. (2015). Bibliometric methods in management and organization. Organizational Research Methods, 18(3), 429–472. https://doi.org/10.1177/1094428114562629