IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i16p6724-d1450933.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/16/6724/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/16/16/6724/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Robert Tomaszewski, 2023. "Visibility, impact, and applications of bibliometric software tools through citation analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(7), pages 4007-4028, July.
    2. Antonio-José Moreno-Guerrero & Jesús López-Belmonte & José-Antonio Marín-Marín & Rebeca Soler-Costa, 2020. "Scientific Development of Educational Artificial Intelligence in Web of Science," Future Internet, MDPI, vol. 12(8), pages 1-18, July.
    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. Manav Raj & Robert Seamans, 2019. "Primer on artificial intelligence and robotics," Journal of Organization Design, Springer;Organizational Design Community, vol. 8(1), pages 1-14, December.
    5. Weishu Liu & Li Tang & Guangyuan Hu, 2020. "Funding information in Web of Science: an updated overview," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(3), pages 1509-1524, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ciurea Iulia-Cristina, 2024. "The Impact of the EU AI Act on the UN Sustainable Development Goals for 2030 – A Text Analysis," Proceedings of the International Conference on Business Excellence, Sciendo, vol. 18(1), pages 2857-2870.
    2. Corsini, Alberto & Pezzoni, Michele, 2023. "Does grant funding foster research impact? Evidence from France," Journal of Informetrics, Elsevier, vol. 17(4).
    3. Lin Zhang & Wenjing Zhao & Beibei Sun & Ying Huang & Wolfgang Glänzel, 2020. "How scientific research reacts to international public health emergencies: a global analysis of response patterns," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(1), pages 747-773, July.
    4. Hui Li & Weishu Liu, 2020. "Same same but different: self-citations identified through Scopus and Web of Science Core Collection," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(3), pages 2723-2732, September.
    5. Li Tang & Jennifer Kuzma & Xi Zhang & Xinyu Song & Yin Li & Hongxu Liu & Guangyuan Hu, 2023. "Synthetic biology and governance research in China: a 40-year evolution," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(9), pages 5293-5310, September.
    6. Weishu Liu & Meiting Huang & Haifeng Wang, 2021. "Same journal but different numbers of published records indexed in Scopus and Web of Science Core Collection: causes, consequences, and solutions," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(5), pages 4541-4550, May.
    7. Kristina McElheran & J. Frank Li & Erik Brynjolfsson & Zachary Kroff & Emin Dinlersoz & Lucia Foster & Nikolas Zolas, 2024. "AI adoption in America: Who, what, and where," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 33(2), pages 375-415, March.
    8. Phanish Puranam, 2021. "Human–AI collaborative decision-making as an organization design problem," Journal of Organization Design, Springer;Organizational Design Community, vol. 10(2), pages 75-80, June.
    9. Shanwu Tian & Xiurui Xu & Ping Li, 2021. "Acknowledgement network and citation count: the moderating role of collaboration network," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(9), pages 7837-7857, September.
    10. Qianqian Jin & Hongshu Chen & Ximeng Wang & Tingting Ma & Fei Xiong, 2022. "Exploring funding patterns with word embedding-enhanced organization–topic networks: a case study on big data," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(9), pages 5415-5440, September.
    11. Valeria Cirillo & Andrea Mina & Andrea Ricci, 2024. "Digital Technologies, Labor market flows and Training: Evidence from Italian employer-employee data," LEM Papers Series 2024/22, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    12. Dario Guarascio & Jelena Reljic & Roman Stollinger, 2023. "Artificial Intelligence and Employment: A Look into the Crystal Ball," LEM Papers Series 2023/34, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    13. Zirar, Araz & Ali, Syed Imran & Islam, Nazrul, 2023. "Worker and workplace Artificial Intelligence (AI) coexistence: Emerging themes and research agenda," Technovation, Elsevier, vol. 124(C).
    14. Monica Aureliana Petcu & Liliana Ionescu-Feleaga & Bogdan-Ștefan Ionescu & Dumitru-Florin Moise, 2023. "A Decade for the Mathematics : Bibliometric Analysis of Mathematical Modeling in Economics, Ecology, and Environment," Mathematics, MDPI, vol. 11(2), pages 1-30, January.
    15. Jasmine Mondolo, 2022. "The composite link between technological change and employment: A survey of the literature," Journal of Economic Surveys, Wiley Blackwell, vol. 36(4), pages 1027-1068, September.
    16. Maha Shaikh & Emmanuelle Vaast, 2023. "Algorithmic Interactions in Open Source Work," Information Systems Research, INFORMS, vol. 34(2), pages 744-765, June.
    17. Salman Bahoo & Marco Cucculelli & Xhoana Goga & Jasmine Mondolo, 2024. "Artificial intelligence in Finance: a comprehensive review through bibliometric and content analysis," SN Business & Economics, Springer, vol. 4(2), pages 1-46, February.
    18. Fang Liu, 2023. "Retrieval strategy and possible explanations for the abnormal growth of research publications: re-evaluating a bibliometric analysis of climate change," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(1), pages 853-859, January.
    19. Yingying Lu & Yixiao Zhou, 2021. "A review on the economics of artificial intelligence," Journal of Economic Surveys, Wiley Blackwell, vol. 35(4), pages 1045-1072, September.
    20. Pablo Casas & Concepción Román, 2024. "The impact of artificial intelligence in the early retirement decision," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 51(3), pages 583-618, August.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:16:y:2024:i:16:p:6724-:d:1450933. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.