AI in Online-Learning Research: Visualizing and Interpreting the Journal Publications from 1997 to 2019


  • Gwo-Jen Hwang National Taiwan University of Science and Technology
  • Yun-Fang Tu Fu Jen Catholic University
  • Kai-Yu Tang Ming Chuan University



artificial intelligence, online learning, literature review, trend analysis, visualization


This study reviews the journal publications of artificial intelligence-supported online learning (AIoL) in the Web of Science (WOS) database from 1997 to 2019 taking into account the contributing countries/areas, leading journals, highly cited papers, authors, research areas, research topics, roles of AIoL, and adopted artificial intelligence (AI) algorithms. Results indicate that, from 1997 to 2009, AIoL research focused on the combination of intelligent tutoring systems and distance learning. In 2010–2014, AIoL research emphasized learner-oriented learning. In 2015–2019, learner-system interactions to facilitate personalized, adaptive, and collaborative learning became the main focus. “Intelligent tutoring systems” have played the most important role in AIoL, followed by “profiling and prediction,” and “adaptive systems with personalization.” Accordingly, the roles and research trends as well as several suggestions for future research in the field of AIoL are provided as a reference for researchers and policy makers.


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How to Cite

Hwang, G.-J., Tu, Y.-F. ., & Tang, K.-Y. (2022). AI in Online-Learning Research: Visualizing and Interpreting the Journal Publications from 1997 to 2019. The International Review of Research in Open and Distributed Learning, 23(1), 104–130.