The Adventure of Artificial Intelligence in Educational Research from the Past to the Present

Abstract

This study aims to examine scientific studies on artificial intelligence (AI) in educational research from the past to the present, based on the Web of Science database. In this context, 1465 scientific articles containing AI in education from the past to the present were evaluated. Articles accessed from the WoS database were examined using a bibliometric analysis method according to productivity, network analyses, conceptual structure, and thematic map titles. Within the scope of productivity, authors, institutions, countries, citations within the scope of network analysis, authors, institutions, sources, and countries were included in the analysis. In addition, thematic changes over the years, word cloud, collaborations, conceptual formations, and thematic mapping were carried out based on keywords. In this context, 1465 scientific articles published by 3783 authors representing 86 countries were included in the research. According to the research findings, the number of studies and citations on AI in education has increased significantly, especially in the last five years. The Education University and The Chinese University of Hong Kong stand out as productive institutions. While China, England, and the USA stand out as the countries of responsible authors, Hwang, G. J., stands out as the author of network analysis, and the Computer Education journal stands out as the journal. As a thematic change in the studies, there has been an evolution towards new technological developments such as deep learning, machine learning, ChatGPT, chatbots, learning analytics, blockchain, and generative AI. According to the factor analysis conducted on the conceptual structure of AI-related studies in education, it was determined that it explained 48% of the total variability. According to the study findings, studies on AI applications in education should be enriched from a disciplinary perspective, and efficiency should be increased regarding their reflections on teaching.

Keywords

• Artificial Intelligence (AI)
• Bibliometric Analysis
• Educational Research
• Web of Science (WoS)

References

1. Aria, M., & Cuccurullo, C. (2017). Bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959-975. https://doi.org/10.1016/j.joi.2017.08.007
2. Arksey, H., & O’Malley, L. (2005). Scoping studies: Towards a methodological framework. International Journal of Social Research Methodology, 8(1), 19-32. https://doi.org/10.1080/1364557032000119616
3. Bağış, M. (2021). Main analysis techniques used in bibliometric research. In O. Öztürk & G. Gürler (Eds.) Bibliometric analysis as a literature review tool (pp. 97-123). Nobel Academic Publishing.
4. Bahroun, Z., Anane, C., Ahmed, V., & Zacca, A. (2023). Transforming education: a comprehensive review of generative artificial intelligence in educational settings through bibliometric and content analysis. Sustainability, 15, 1-40. https://doi.org/10.3390/su151712983
5. Bayne, S. (2015). Teacherbot: Interventions in automated teaching. Teaching in Higher Education, 20(4), 455-467. https://doi.org/10.1080/13562517.2015.1020783
6. Bozkurt, A., Karadeniz, A., Baneres, D., Guerrero-Roldán, A. E., & Rodríguez, M. E. (2021). Artificial intelligence and reflections from educational landscape: A review of AI studies in half a century. Sustainability, 12(2), 1-16. https://doi.org/10.3390/su13020800
7. Chaparro, N., & Rojas-Galeano, S. (2021). Revealing the research landscape of master’s degrees via bibliometric analyses. Library Philosophy and Practice, arXiv:2103.09431. https://doi.org/10.48550/arXiv.2103.09431
8. Chatti, M. A., Dyckhoff, A. L., Schoeder, U., & Thüs, H. (2012). A reference model for learning analytics. International Journal of Technology Enhanced Learning, 4(5/6), 318-331. https://doi.org/10.1504/IJTEL.2012.051815

9. Chen, H. E., Sun, D., Hsu, T. C., Yang, Y., & Sun, J. (2023). Visualising trends in computational thinking research from 2012 to 2021: A bibliometric analysis. Thinking Skills and Creativity, 47, 1-18. https://doi.org/10.1016/j.tsc.2022.101224
10. Chen, X., Zou, D., Xie, H., Cheng, G., & Liu, C. (2022). Two decades of artificial intelligence in education: Contributors, collaborations, research topics, challenges, and future directions. Educational Technology & Society, 25(1), 28-47. https://doi.org/10.30191/ETS.202201_25(1).0003
11. Chen, L., Chen, P., & Lin, Z. (2020). Artificial intelligence in education: A review. IEEE Access, 8, 75264–75278. https://doi.org/10.1109/ACCESS.2020.2988510
12. Chen, X., Yu, G., Cheng, G., & Hao, T. (2019). Research topics, author profiles, and collaboration networks in the top-ranked journal on educational technology over the past 40 years: A bibliometric analysis. Journal of Computers in Education, 6(4), 563-585. https://doi.org/10.1007/s40692-019-00149-1
13. Chiu, T. K. F., & Chai, C. S. (2020). Sustainable curriculum planning for artificial intelligence education: A self-determination theory perspective. Sustainability, 12(14), 1-18. https://doi.org/10.3390/su12145568
14. Cobo, M., Lopez-Herrera, A., Herrera-Viedma, E., & Herrera, F. (2011). An approach for detecting, quantifying, and visualizing the evolution of a research field: A practical application to the fuzzy sets theory field. Journal of Informetrics, 5(1), 146-166. https://doi.org/10.1016/j.joi.2010.10.002
15. Cui, W., Xue, Z., & Thai, K. P. (2018, November). Performance comparison of an AI-based adaptive learning system in China. In 2018 Chinese automation congress (CAC) (pp. 3170-3175). IEEE.
16. Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & 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
17. Fang, C., Zhang, J., & Qiu, W. (2017). Online classified advertising: A review and bibliometric analysis. Scientometrics, 113(3), 1481-1511. https://doi.org/10.1007/s11192-017-2524-6
18. Findlay, K., & van Rensburg, O. (2018). Using interaction networks to map communities on Twitter. International Journal of Market Research, 60(2), 169-189. https://doi.org/10.1177/1470785317753025
19. Forero-Corba, W., & Negre-Bennasar, F. (2024). Techniques and applications of machine learning and artificial intelligence in education: A systematic review. RIED-Revista Iberoamericana de Educación a Distancia, 27(1), 209-253. https://doi.org/10.5944/ried.27.1.37491
20. Garcia, P., Amandi, A., Schiaffino, S., & Campo, M. (2007). Evaluating Bayesian networks’ precision for detecting students’ learning styles. Computers & Education, 49(3), 794-808. https://doi.org/10.1016/j.compedu.2005.11.017
21. Gonzales-Valiente, C. (2019). Redes de citación de revistas iberoamericanas de bibliotecología y ciencia de la información en Scopus [Citation networks of Ibero-American library and information science journals in Scopus]. Bibliotecas Anales de Investigación, 15, 83-98. Retrieved from http://agora.edu.es/servlet/articulo?codigo=7871010
22. Grzybowska, K., & Awasthi, A. (2020). Literature review on sustainable logistics and sustainable production for industry 4.0. In K., Grzybowska, A., Awasthi, & R., Sawhney (Eds.), Sustainable logistics and production in industry 4.0 new opportunities and challenges (pp. 1-19). Springer Publishing.
23. Harmon, J., Pitt, V., Summons, P., & Inder, K. J. (2021). Use of artificial intelligence and virtual reality within clinical simulation for nursing pain education: A scoping review. Nurse Education Today, 97, 1-9. https://doi.org/10.1016/j.nedt.2020.104700
24. Hinojo-Lucena, F. J., Aznar-Díaz, I., Cáceres-Reche, M. P., & Romero-Rodríguez, J. M. (2019). Artificial intelligence in higher education: A bibliometric study on its impact in the scientific literature. Education Sciences, 9(1), 1-9. https://doi.org/10.3390/educsci9010051
25. Huang, X. (2021). Aims for cultivating students’ key competencies based on artificial intelligence education in China. Education and Information Technologies, 26, 5127-5147. https://doi.org/10.1007/s10639-021-10530-2
26. Huang, X., & Qiao, C. (2024). Enhancing computational thinking skills through artificial intelligence education at a STEAM high school. Science & Education, 33(2), 383-403. https://doi.org/10.1007/s11191-022-00392-6
27. Huang, J., Shen, G., & Ren, X. (2021a). Connotation analysis and paradigm shift of teaching design under artificial intelligence technology. International Journal of Emerging Technologies in Learning (iJET), 16(5), 73-86. https://doi.org/10.3991/ijet.v16i05.20287
28. Huang, J., Saleh, S., & Liu, Y. (2021b). A review on artificial intelligence in education. Academic Journal of Interdisciplinary Studies, 10(3), 206-217. https://doi.org/10.36941/ajis-2021-0077
29. Hwang, G. J., & Tu, Y. F. (2021). Roles and research trends of artificial intelligence in mathematics education: A bibliometric mapping analysis and systematic review. Mathematics, 9(6), 1-19. https://doi.org/10.3390/math9060584
30. 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, 1-5. https://doi.org/10.1016/j.caeai.2020.100001
31. Jamali, S. M., Ebrahim, N. A., & Jamali, F. (2022). The role of STEM education in improving the quality of education: A bibliometric study. International Journal of Technology and Design Education, Springer Verlag, 32(3), 1-22. https://doi.org/10.1007/s10798-022-09762-1
32. Kohonen, T. (2001). Self-organizing maps (3rd ed.). Springer-Verlag. http://dx.doi.org/10.1007/978-3-642-56927-2
33. Law, J., Bauin, S., Courtial, J., & Wittaker, J. (1988). Policy and the mapping of scientific change: A co-word analysis of research into environmental acidification. Scientometrics, 14(3-4), 251-264. https://doi.org/10.1007/BF02020078
34. Lee, H. S., & Lee, J. (2021). Applying artificial intelligence in physical education and future perspectives. Sustainability, 13(1), 1-16. https://doi.org/10.3390/su13010351
35. Lemaignan, S., Warnier, M., Sisbot, E. A., Clodic, A., & Alami, R. (2017). Artificial cognition for social human–robot interaction: An implementation. Artificial Intelligence, 247, 45–69. https://doi.org/10.1016/j.artint.2016.07.002
36. Li, Z., & Wang, H. (2021). The effectiveness of physical education teaching in college based on Artificial intelligence methods. Journal of Intelligent & Fuzzy Systems, 40(2), 3301-3311. https://doi.org/10.3233/JIFS-189370
37. Liao, H., Tang, M., Li, Z., & Lev, B. (2019). Bibliometric analysis for highly cited papers in operations research and management science from 2008 to 2017 based on essential science indicators. Omega, 88, 223-236. https://doi.org/10.1016/j.omega.2018.11.005
38. Lin, Y. S., Chen, S. Y., Tsai, C. W., & Lai, Y. H. (2021). Exploring computational thinking skills training through augmented reality and AIoT learning. Frontiers in Psychology, 12, 1-9. https://doi.org/10.3389/fpsyg.2021.640115
39. McCulloch, W. S., & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. The Bulletin of Mathematical Biophysics, 5(4), 115-133. https://doi.org/10.1007/BF02478259
40. Mostafa, M. M. (2022). Three decades of interactive learning environments: A retrospective bibliometric network analysis. Interactive Learning Environments, 31(10), 6968-6987. https://doi.org/10.1080/10494820.2022.2057548
41. Mostafa, M. M. (2020). A knowledge domain visualization review of thirty years of halal food research: Themes, trends and knowledge structure. Trends in Food Science & Technology, 99, 660-677. https://doi.org/10.1016/j.tifs.2020.03.022
42. Organisation for Economic Co-operation and Development (OECD) (2019). Artificial intelligence in society (pp. 47-80). OECD Publishing. https://doi.org/10.1787/eedfee77-en
43. Paek, S., & Kim, N. (2021). Analysis of worldwide research trends on the impact of artificial intelligence in education. Sustainability, 13(14), 1-20. https://doi.org/10.3390/su13147941
44. Park, S., Lim, Y., & Park, H. (2015). Comparing Twitter and YouTube networks in information diffusion: The case of the "occupy wall street" movement. Technological Forecasting and Social Change, 95(6), 208-217. https://doi.org/10.1016/j.techfore.2015.02.003
45. Popenici, S. A. D., & Kerr, S. (2017). Exploring the impact ofartificial intelligence on teaching and learning in higher education. Research and Practice in Technology Enhanced Learning, 12(1), 1-13. https://doi.org/10.1186/s41039-017-0062-8
46. Price, D. J. (1963). Little science, big science. Columbia University Press.
47. Pritchard, A. (1969). Statistical bibliography or bibliometrics. Journal of Documentation, 25(4), 348-349. Retrieved from https://cir.nii.ac.jp/crid/1570009750342049664
48. Pretorius, L. (2023). Fostering AI literacy: A teaching practice reflection. Journal of Academic Language & Learning, 17(1), 1-8. Retrieved from https://research.monash.edu/en/publications/fostering-ai-literacy-a-teaching-practice-reflection
49. Russell, S. J., & Norvig, P. (2021). Artificial intelligence: A modern approach (4th ed.). Pearson Publishing.
50. Shan, S., & Liu, Y. (2021). Blended teaching design of college students’ mental health education course based on artificial intelligence flipped class. Mathematical Problems in Engineering, 2021, 1-10. https://doi.org/10.1155/2021/6679732
51. Shi, S. J., Li, J. W., & Zhang, R. (2024). A study on the impact of generative artificial intelligence supported situational interactive teaching on students’ ‘flow’ experience and learning effectiveness-a case study of legal education in China. Asia Pasific Journal of Education, 44(1), 112-138. https://doi.org/10.1080/02188791.2024.2305161
52. Tsai, S. C., Chen, C. H., Shiao, Y. T., Ciou, J. S., & Wu, T. N. (2020). Precision education with statistical learning and deep learning: A case study in Taiwan. International Journal of Educational Technology in Higher Education, 17(1), 1-13. https://doi.org/10.1186/s41239-020-00186-2
53. Tobler, S. (2024). Smart grading: A generative AI-based tool for knowledge-grounded answer evaluation in educational assessments. MethodsX, 12, 1-6. https://doi.org/10.1016/j.mex.2023.102531
54. United Nations Educational, Scientific and Cultural Organization (UNESCO) (2021). Intergovernmental meeting of experts (category ll) related to a draft recommendation on the ethics of artificial intelligence. Retrieved from https://unesdoc.unesco.org/ark:/48223/pf0000376712/PDF/376-712-eng.pdf.multi
55. Van Eck N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523-538. https://doi.org/10.1007/s11192-009-0146-3
56. Verbeek, A., Debackere, K., Luwel, M., & Zimmermann, E. (2002). Measuring progress and evolution in science and technology-I: The multiple uses of bibliometric indicators. International Journal of Management Reviews, 4(2), 179-211. https://doi.org/10.1111/1468-2370.00083
57. Wang, S., Sun, Z., & Chen, Y. (2023). Effects of higher education institutes’ artificial intelligence capability on students’ self‑efficacy, creativity and learning performance. Education and Information Technologies, 28, 4919-4939. https://doi.org/10.1007/s10639-022-11338-4
58. Web of Science Group (WoSG) (2024). Web of Science Core Collection. Retrieved from https://clarivate.com/
59. Wetzstein, A., Feisel, E., Hartmann, E., & Benton, W. (2019). Uncovering the supplier selection knowledge structure: A systematic citation network analysis from 1991 to 2017. Journal of Purchasing and Supply Management, 25(4), 1-16. https://doi.org/10.1016/j.pursup.2018.10.002
60. Wong, W., Mittas, N., Arvanitou, E., & Li, Y. (2021). A bibliometric assessment of software engineering themes, scholars and institutions (2013-2020). Journal of Systems and Software, 180, 1-10. https://doi.org/10.1016/j.jss.2021.111029
61. Xie, H., Chu, H. C., Hwang, G. J., & Wang, C. C. (2019). Trends and development in technology-enhanced adaptive/ personalized learning: A systematic review of journal publications from 2007 to 2017. Computers & Education, 140, 1-16. https://doi.org/10.1016/j.compedu.2019.103599
62. Yang, J., & Zhang, B. (2019). Artificial intelligence in intelligent tutoring robots: A systematic review and design guidelines. Applied Sciences, 9(10), 1-18. https://doi.org/10.3390/app9102078
63. Yeoh, W., Talburt, J., & Zhou, Y. (2013). Information quality and governance for business intelligence. IGI Global Publishing.
64. Yuan, B. Z., Bie, Z. L., & Sun, J. (2021). Bibliometric analysis of global research on muskmelon (Cucumis melo L.) based on web of science. Hort Science, 56(8), 867-874. https://doi.org/10.21273/HORTSCI15827-21
65. Zhao, S., Shen, Y., & Qi, Z. (2023). Research on chatgpt-driven advanced mathematics course. Academic Journal of Mathematical Sciences, 4(5), 42-47. https://doi.org/10.25236/AJMS.2023.040506
66. Zhao, D., & Strotmann, A. (2015). Analysis and visualization of citation networks. Synthesis Lectures on Information Concepts, Retrieval, and Services, 7(1), 1-207. https://doi.org/10.1007/978-3-031-02291-3