Knowledge Mapping for the Study of Artificial Intelligence in Education Research: Literature Reviews

Authors

  • Thitinan Chankoson
  • Fenglei Chen fenglei.che@rmutto.ac.th
  • Zhiting Wang
  • Mengqi Wang
  • Khunanan Sukpasjaroen

DOI:

https://doi.org/10.37380/jisib.v12i3.896

Abstract

This study aims to provide a systematic and complete knowledge map for researchers working in the field of research on the application of artificial intelligence in education. In addition, it is designed to help researchers quickly understand author collaboration characteristics, institutional collaboration characteristics, trending research topics, evolutionary trends, and research frontiers of scholars from a library informatics perspective. In this study, a bibliometric approach was used to quantitatively analyze the retrieved literature with the help of the bibliometric analysis software CiteSpace. The analysis results are presented in tables and visual images in this paper. The results of this study indicate that collaborative relationships among scholars need to be improved and collaborative research relationships among research institutions are more fragmented. This study also points out the shortcomings of this study: Chinese educational researchers and practitioners still have a relatively vague understanding of some fundamental issues in the process of integration and development of AI and education. Therefore, this paper uses quantitative research methods such as bibliometrics and visualization pictures to systematically and intuitively reveal the research progress and trends on the application of artificial intelligence in education based on the published literature and to provide a reference for further research on this topic in the future.

References

Barabasi AL, Albert R. Emergence cf scaling in random networks.Science. 1999,286:509-512.

Chen C. Searching for intellectual turning points: Progressive knowledge domain visualization. Proceedings of the National Academy of Sciences of the United States of America (PNAS.2004:5303-5310.

Chen Chaomei.Cite Space II: Deteching and visualizing emerging trends and transient patterns in scientific literature. Journal of the American Society for Information Science and Technology,2006,57(3):2007

Dai YF, Xu B, Chen HJ (2018). The promotion of artificial intelligence for blended teaching and ecological chain construction. Modern distance education research,(2):24-31.

Jia, Jing-Yuan (2016). Study the Current Situation and Countermeasures of Special Education Informatization Construction in Henan Province. Kaifeng: Henan University.

Leun C, Tang XIA, Qin Xuan et al. (2017). Implications, Key Technologies and Application Trends of Educational Artificial Intelligence (EAI)-An Analysis of the U.S. Preparing for the Future of Artificial Intelligence and the National Artificial Intelligence R&D Strategic Plan Reports. Journal of Distance Education, (1):26-35.

Li Yan. (2011). The practice and variation of journalistic professionalism in mainland China. Contemporary Communication, (1), 4-7.

Li Z, Zhou D, Liu N, et al. (2018). Platform Construction and Key Implementation Technologies of Educational Big Data. Modern Educational Technology, (1): pp. 100–106.

Liu Y, Huang Chuanhui.Embedded User Environments: New Directions for Library Subject Services. Library Information Knowledge.2010, (1):52 -53.

Liu Y, Li Q, Yu Cui-Bo (2017). Deep learning technology educational applications: current status and prospects. Open Education Research,(5):113-120.

Monostori, L. (2014). Artificial Intelligence[A].Laperriere, L.,&Reinhart, G. (2014). CIRP Encyclopedia of Production Engineering[C]. Berlin: Springer:2-39.

State Council (2017a). Notice of the State Council on the issuance of the development plan for a new generation of artificial intelligence. [2018-01-25]. http://www.gov.cn/zhengce/content/2017-07/20/content 5211996. htm.

State Council (2017b). Notice of the State Council issuance of the "Thirteenth Five-Year Plan" for the development of national education. [2018-01-25].http://www.gov.cn/zhengce/content/2017-01/19/content-5161341.htm.

Wang Chengbo, Li Xiaoping, Zhao Fengnian (2015). Research on fragmented learning in the era of big data. Research on Electrochemical Education,(10): pp. 26–30.

Wang P, Shi L, Chen Zhanjin (2018). Intelligent virtual assistant: Analysis and design of a new learning support system. Electrochemical Education Research,(2):1-6.

White House(2016). Preparing for the Future of Artificial Intelligence. [2018-01-25].https://obamawhite-house.archives.gov/blog/2016/OS/03/preparing-future-artifi-cial-intelligence.

Wu YH, Liu BW, Ma XL (2017). Constructing an ecosystem of "artificial intelligence + education." Journal of Distance Education,(5):27-39.

Xing Beibei, Yang Xianmin, Li Qinsheng (2016). Sources and collection techniques of big educational data. Modern Educational Technology,(8):14-21.

Yan, C., Li, H., Pu, R., Deeprasert, J., & Jotikasthira, N. (2022). Knowledge mapping of research data in China: a bibliometric study using visual analysis. Library Hi Tech, (ahead-of-print).

Yu M. H., Feng X., Zhu Z. T. (2017). Exploration of Educational Applications and Innovations of Machine Learning in the Perspective of Artificial Intelligence. Journal of Distance Education,(3):11-21.

Zhan Xinyi, Wang Xia. Application of artificial intelligence in computer network technology in the era of big data. Science and Technology Innovation and Application,2020(33):168-169.

Downloads

Published

2023-03-09