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

Year 2024, Volume: 14 Issue: 3, 447 - 472
https://doi.org/10.19126/suje.1474955

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.

Ethical Statement

In the entire process from planning to implementation of this research, from data collection to data analysis, all rules specified within the scope of the Higher Education Institutions Scientific Research and Publication Ethics Directive were complied with and no damage was done to the data set. Ethics and citation rules were followed during the writing process and it was not sent to any other academic publication environment for evaluation. Additionally, since the study is not conducted on humans, it does not require ethics committee permission due to its method and scope.

References

  • 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
  • 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
  • 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.
  • 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
  • Bayne, S. (2015). Teacherbot: Interventions in automated teaching. Teaching in Higher Education, 20(4), 455-467. https://doi.org/10.1080/13562517.2015.1020783
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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.
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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.
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • Kohonen, T. (2001). Self-organizing maps (3rd ed.). Springer-Verlag. http://dx.doi.org/10.1007/978-3-642-56927-2
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • Price, D. J. (1963). Little science, big science. Columbia University Press.
  • Pritchard, A. (1969). Statistical bibliography or bibliometrics. Journal of Documentation, 25(4), 348-349. Retrieved from https://cir.nii.ac.jp/crid/1570009750342049664
  • 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
  • Russell, S. J., & Norvig, P. (2021). Artificial intelligence: A modern approach (4th ed.). Pearson Publishing.
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • Web of Science Group (WoSG) (2024). Web of Science Core Collection. Retrieved from https://clarivate.com/
  • 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
  • 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
  • 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
  • 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
  • Yeoh, W., Talburt, J., & Zhou, Y. (2013). Information quality and governance for business intelligence. IGI Global Publishing.
  • 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
  • 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
  • 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
Year 2024, Volume: 14 Issue: 3, 447 - 472
https://doi.org/10.19126/suje.1474955

Abstract

References

  • 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
  • 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
  • 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.
  • 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
  • Bayne, S. (2015). Teacherbot: Interventions in automated teaching. Teaching in Higher Education, 20(4), 455-467. https://doi.org/10.1080/13562517.2015.1020783
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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.
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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.
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • Kohonen, T. (2001). Self-organizing maps (3rd ed.). Springer-Verlag. http://dx.doi.org/10.1007/978-3-642-56927-2
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • Price, D. J. (1963). Little science, big science. Columbia University Press.
  • Pritchard, A. (1969). Statistical bibliography or bibliometrics. Journal of Documentation, 25(4), 348-349. Retrieved from https://cir.nii.ac.jp/crid/1570009750342049664
  • 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
  • Russell, S. J., & Norvig, P. (2021). Artificial intelligence: A modern approach (4th ed.). Pearson Publishing.
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • Web of Science Group (WoSG) (2024). Web of Science Core Collection. Retrieved from https://clarivate.com/
  • 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
  • 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
  • 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
  • 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
  • Yeoh, W., Talburt, J., & Zhou, Y. (2013). Information quality and governance for business intelligence. IGI Global Publishing.
  • 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
  • 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
  • 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
There are 66 citations in total.

Details

Primary Language English
Subjects Educational Technology and Computing
Journal Section Articles
Authors

Deniz Kaya 0000-0002-7804-1772

Early Pub Date November 4, 2024
Publication Date
Submission Date April 28, 2024
Acceptance Date September 11, 2024
Published in Issue Year 2024 Volume: 14 Issue: 3

Cite

APA Kaya, D. (2024). The Adventure of Artificial Intelligence in Educational Research from the Past to the Present. Sakarya University Journal of Education, 14(3), 447-472. https://doi.org/10.19126/suje.1474955