Bibliometric review on teaching methods with artificial intelligence in education

Raúl Alberto Garcia Castro 1 * , Gilber Chura-Quispe 2, Jehovanni Fabricio Velarde Molina 2, Luis Alberto Espinoza Ramos 1, Catherine Alessandra Almonte Durand 1
More Detail
1 Universidad Nacional Jorge Basadre Grohmann, Tacna, PERU
2 Escuela de Posgrado Newman, Tacna, PERU
* Corresponding Author
Online Journal of Communication and Media Technologies, Volume 14, Issue 2, Article No: e202419.
OPEN ACCESS   620 Views   296 Downloads   Published online: 14 Mar 2024
Download Full Text (PDF)


The purpose of this article is to carry out an analysis of the disclosures made on teaching methods applying artificial intelligence in the Scopus database. The bibliometric review method was used to analyze 349 scientific articles dating from 1978 to 2023. The analysis was carried out using Bibliometrix and VOSviewer software, and the results show that from 2021 onwards there will be a notable increase in publications, with Mobile Information Systems being the journal with the highest production. Among 65 countries identified, China is the country with the highest production and the most productive organization was the Ministry of Education of the People’s Republic of China. No single author stands out for his or her highest scientific output, given that the maximum number of articles published per author is two. However, among the most cited authors is Alimisis, D. and the most co-cited author is Wang, Y. In terms of co-authorship, there is little contribution between authors, while collaboration between countries, China together with Hong Kong, Japan, Malaysia, Mexico, South Korea, Taiwan, Thailand form the most collaborative conglomerate. Cooperation between institutions, the division of computer engineering and the National University of Singapore, show the strongest collaboration. The strongest keywords are “artificial intelligence”, followed by “teaching methods” and “machine learning” and the topics that will be trending from 2021 onwards are “machine learning”, “ChatGPT”, “deep learning”.


Garcia Castro, R. A., Chura-Quispe, G., Velarde Molina, J. F., Espinoza Ramos, L. A., & Almonte Durand, C. A. (2024). Bibliometric review on teaching methods with artificial intelligence in education. Online Journal of Communication and Media Technologies, 14(2), e202419.


  • Abdel, K., & Bastami, D. (2012). Estrategias de enseñanza [Teaching strategies]. Casa Al-Mutanabbi.
  • Aktepe, V., Tahiroglu, M., & Acer, T. (2015). Matematik öğretiminde kullanılan öğretim yöntemlerine ilişkin öğrenci görüşleri [Student opinions regarding the teaching methods used in teaching mathematics]. Nevşehir Hacı Bektaş Veli Üniversitesi Sosyal Bilimler Enstitüsü Dergisi [Nevsehir Haci Bektas Veli University Social Sciences Institute Journal], 4(2), 27-143.
  • Al-Ghasab, G. B. (2022). Reality of using modern teaching methods in teaching English language among teachers. International Journal of Education in Mathematics, Science and Technology, 10(2), 512-527.
  • Briner, R., & Demyer, D. (2012). Systematic review and evidence synthesis as a practice and scholarship tool. In D. M. Rousseau (Ed.), The Oxford handbook of evidence-based management (pp. 112-129). Oxford Library of Psychology.
  • Chamorro-Atalaya, O., Olivares-Zegarra, S., Sobrino-Chunga, L., Guerrero-Carranza, R., Vargas-Diaz, A., Huarcaya-Godoy, H., Rasilla-Rovegno, J., Suarez-Bazalar, J., & Poma-Garcia, J. (2023). Application of the chatbot in university education: A bibliometric analysis of indexed scientific production in Scopus, 2013-2023. International Journal of Learning, Teaching and Educational Research, 22(7), 281-304.
  • Chura-Quispe, G., & Garcia Castro, R. A. (2024). A techno-pedagogical design for the production of academic essays in university students. Contemporary Educational Technology, 16(1), ep486.
  • Cukurova, M., Kent, C., & Luckin, R. (2019). Artificial intelligence and multimodal data in the service of human decision-making: A case study in debate tutoring. British Journal of Educational Technology, 50(6), 3032-3046.
  • Eguchi, A. (2022). AI-powered educational robotics as a learning tool to promote artificial intelligence and computer science education. Robotics in Education, 1359(1), 279-287.
  • El Hajj, M., & Harb, H. (2023). Rethinking education: An in-depth examination of modern technologies and pedagogic recommendations. IAFOR Journal of Education, 11(2), 97-113.
  • Esti, M., Kuswanto, H., Suyanto, S., Purwasih, D., & Prabawati, R. (2023). A bibliometric review of research on education for sustainable development, 2019-2023. International Electronic Journal of Elementary Education, 1(16), 75-78.
  • Govindan, K., & Hasanagic, M. (2018). A systematic review on drivers, barriers, and practices towards circular economy: A supply chain perspective. International Journal of Production Research, 52(1-2), 278-311.
  • Hamilton, D., McKechnie, J., Edgerton, E., & Wilson, C. (2020). Immersive virtual reality as a pedagogical tool in education: a systematic literature review of quantitative learning outcomes and experimental design. Journal of Computers in Education, 8(1), 1-32.
  • Hwang, G. J., Xie, H., Wah, B. W., & Gašević, D. (2020). Vision, challenges, roles and research issues of artificial intelligence in education. Computers and Education: Artificial Intelligence, 1, 100001.
  • Kaban, A. (2023). Artificial intelligence in education: A science mapping approach. International Journal of Education in Mathematics, Science, and Technology, 11(4), 844-861.
  • Karsenti, T. (2019). Artificial intelligence in education: The urgent need to prepare teachers for tomorrow’s schools. Formation et Profession [Training and Profession], 27(1), 105.
  • Kong, F. (2020). Application of artificial intelligence in the teaching of modern art. International Journal of Emerging Technologies in Learning, 15(13), pp. 238-251.
  • Lave, J., & Wenger, E. (1991). Situated learning: Legitimate peripheral participation. Cambridge University Press.
  • Mathiyazhagan, K., Rajak, S., Sampurna Panigrahi, S., Agarwal, V., & Manani, D. (2021). Reverse supply chain management in manufacturing industry: A systematic review. International Journal of Productivity and Performance Management, 70(4), 859-892.
  • Moreno-Guerrero, A., López-Belmonte, J., Marín-Marín, J., & Soler-Costa, R. (2020). Scientific development of educational artificial intelligence in web of science. Future Internet, 12(8), 124.
  • Nahar, K., Shova, B. l., Ria, T., Binte, H., & Saifull, I. (2021). Mining educational data to predict students performance. Education and Information Technologies, 26(5), 6051-6067.
  • Nérici, I. (1985). Metodología de la enseñanza [Teaching methodology]. Kapelusz Mexicana.
  • Nguyen, T., Nguyen, M., & Hoang T. (2023). Artificial intelligent based teaching and learning approaches: A comprehensive review. International Journal of Evaluation and Research in Education, 12(4), 2387-2400.
  • Perianes-Rodriguez, A., Waltman, L., & van Eck, N. (2016). Constructing bibliometric networks: A comparison between full and fractional counting. Journal of Informetrics, 10(4), 1178-1195.
  • Prahani, B. K., Rizki, I. A., Jatmiko, B., Suprapto, N., & Tan, A. (2022). Artificial intelligence in education research during the last ten years: A review and bibliometric study. International Journal of Emerging Technologies in Learning, 17(08), 169-188.
  • Pu, S., Ahmad, N. A., Khambari, M. N. M., & Yap, N. K. (2021). Identification and analysis of core topics in educational artificial intelligence research: A bibliometric analysis. Cypriot Journal of Educational Sciences, 16(3), 995-1009.
  • Ruiz-Rojas, L. I., Acosta, P., De-Moreta-Llovet, J., & González, M. (2023). Empowering education with generative artificial intelligence tools: Approach with an instructional design matrix. Sustainability, 15(1), 11524.
  • Rycroft-Malone, J., McCormack, B., Hutchinson, A. M., DeCorby, K., Bucknall, T. C., Kent, B., & Wilson, V. (2012). Realist synthesis: Illustrating the method for implementation research. Implementation Science, 7, 33.
  • Shen, G., Yang, S., Huang, Z., Yu, Y., & Li, X. (2022). Predicting programming performance using student profiles. Education and Information Technologies, 28(1), 725-740.
  • Song, P., & Wang, X. A. (2020). A bibliometric analysis of worldwide educational artificial intelligence research development in recent twenty years. Asia Pacific Education Review, 21(3), 473-486.
  • Talan, T. (2021). Artificial intelligence in education: A bibliometric study. International Journal of Research in Education and Science, 7(3), 822-837.
  • van Eck, N. J., & Waltman, L. (2023). VOSviewer manual.
  • Zupic, I., & Cater, T. (2015). Bibliometric methods in management and organization: A review. Academy of Management, 18(3), 429-472.