A bibliometric study on artificial intelligence in education for two decades

A bibliometric study on artificial intelligence in education for two decades

• Chen, Xieling The Education University of Hong Kong
• Zou, Di The Education University of Hong Kong
• Xie, Haoran Lingnan University
• Cheng, Kwok Shing The Education University of Hong Kong
• Liu, Caixia Nanjing Normal University

The International Conference on Education and Artificial Intelligence 2020 (ICEAI 2020) (2020: Hong Kong)

• F Learning & Teaching
  • FD Application of Communicative Media, IT in Learning & Teaching

Artificial Intelligence in Education Structural Topic Modeling Bibliometric Analysis Research Topics Research Evolution

• Hong Kong

• 1997.7 onwards

• Unknown or Unspecified

BACKGROUND: Research on Artificial Intelligence in Education (AIEd) has become increasingly active during the past 20 years, with great interest, rich literature, and huge diversity. The diversity in research topics and technologies keeps increasing along with the tremendous growth in the application scope of AIEd research. A comprehensive overview of this field is necessary to understand what had been concerned in AIEd research, what is going on in the field of AIEd, and what might be the future of AIEd research. METHODS: This paper combined the structural topic modeling (STM) with the bibliometric analysis to automatically identify prominent research topics from the large-scale AIEd literature in the past two decades. Specifically, by using STM, we uncovered important research topics concerned within the AIEd community. The evolution of these topics was explored by using a nonparametric Mann-Kendall (MK) trend test. In addition, the correlation between these topics was further explored and visualized based on a semi-parametric Gaussian procedure. RESULTS: The annual trend of publications concerning AIEd had grown consistently across the past 20 years, indicating that research on AIEd had received a growing interest from academia Analyses on topical trends, correlations, and clusters reveal distinct developmental trends of these topics and promising research orientations. Specifically, the top six topics having the highest proportions in the dataset were cognition and perception, programming education, prediction, MOOCs, semantic Web and recommender systems, assessment and feedback, and emotion detection. Results of the MK test highlighted five topics receiving an increase in research interest, including prediction, assessment and feedback, flipped classroom and biosignal data, STEM education, and English language learning. In addition, several inter-topic research directions were identified, including 1) flipped classroom and biosignal data, emotion detection, and game-based learning, 2) artificial intelligence algorithms and prediction, 3) mobile and robotics-based learning and STEM education, as well as 4) English language learning, MOOCs, semantic Web and recommender systems, assessment and feedback, and natural language processing. CONCLUSIONS: The topic-based bibliometric analysis contributed to the community of AIEd by providing a comprehensive overview. The exploration of important topics, topic prevalence and developments, and emerging inter-topic directions helps identify and compare current and potential scientific strengths. These findings help educators and researchers promote current and potential competitive research areas and enhance scientific communication and collaborations with promising countries/regions or institutions in specific research areas to bolster the scientific activities of AIEd. Copyright © 2020 The Education University of Hong Kong (EdUHK).

Paper presented at The International Conference on Education and Artificial Intelligence 2020 (ICEAI 2020), Hong Kong, China.

• English

Conference Papers

https://bibliography.lib.eduhk.hk/en/bibs/d8062682

2022-02-11