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Understanding the Growth of Artificial Intelligence in Educational Research through Bibliometric Analysis

Author

Listed:
  • Ibrahim Delen

    (Mathematics and Science Education Department, University of Usak, 64200 Usak, Türkiye)

  • Nihal Sen

    (Institute of Educational Sciences, Special Education, Bolu Abant Izzet Baysal University, 14030 Bolu, Türkiye)

  • Fatma Ozudogru

    (Educational Sciences Department, University of Usak, 64200 Usak, Türkiye)

  • Michele Biasutti

    (Department of Philosophy, Sociology, Education and Applied Psychology, University of Padova, 35139 Padova, Italy)

Abstract

The purpose of this study was to investigate research trends in artificial intelligence studies related to education that were published in the Web of Science database. This review conducted a bibliometric analysis of 4673 articles published between 1975 and 2023 and explored trends in several areas, including the annual distribution of publications, frequently studied topics, top authors, top countries, top universities/departments, top journals and publishers, and top funders. The findings highlighted that the number of studies increased exponentially after 2010. The most often used terms in artificial intelligence research in education were machine learning, deep learning, and data mining. Studies in higher education have been more prevalent than studies in elementary and secondary education. The USA, mainland China, and Australia were the three most productive nations. Most productive authors were connected to academic institutions in Taiwan, Hong Kong, or mainland China. Furthermore, there was little cooperation among the most productive authors andcountries. In addition to the abundance of journals on educational technology, it is crucial to emphasize the dearth of publications on education across different disciplines. To understand how artificial intelligence can support new practices in educational research, interdisciplinary interest and support are needed.

Suggested Citation

  • Ibrahim Delen & Nihal Sen & Fatma Ozudogru & Michele Biasutti, 2024. "Understanding the Growth of Artificial Intelligence in Educational Research through Bibliometric Analysis," Sustainability, MDPI, vol. 16(16), pages 1-16, August.
  • Handle: RePEc:gam:jsusta:v:16:y:2024:i:16:p:6724-:d:1450933
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    References listed on IDEAS

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    3. Mila Cascajares & Alfredo Alcayde & Esther Salmerón-Manzano & Francisco Manzano-Agugliaro, 2021. "The Bibliometric Literature on Scopus and WoS: The Medicine and Environmental Sciences Categories as Case of Study," IJERPH, MDPI, vol. 18(11), pages 1-31, May.
    4. Glenn S. McGuigan & Göktuğ Morçöl & Travis Grosser, 2023. "A social network analysis of academic journals in public administration in the early twenty-first century: examining journal level bibliometrics with network analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(12), pages 6561-6588, December.
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    Cited by:

    1. Emilio López & Maria de Fátima Oliveira & Pedro Reis, 2025. "The Use of Circular Economy in Horticulture Research: A Bibliometric Analysis," Sustainability, MDPI, vol. 17(7), pages 1-16, April.

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