Artificial Intelligence in Online Learning: Using BERTopic to Track Research Topics and Their Evolutions

Authors

DOI:

https://doi.org/10.19173/irrodl.v27i3.9516

Keywords:

artificial intelligence, online learning, BERTopic, research topics, topic evolutions

Abstract

Artificial intelligence in online learning (AIOL) has attracted increasing attention in academia. This study applied BERTopic, a transformer-based topic modeling approach that leverages contextual embeddings, to examine the thematic structure and evolution of AIOL research. By analyzing 1,048 AIOL publications, this study sought to answer three research questions. What are the major research topics in AIOL? How have these topics evolved over time? What future research directions are recognized? Several research themes were identified, such as adaptive learning systems, sentiment analysis, and predictive analytics. Temporal analysis revealed a shift from early applications of traditional AI toward machine learning and deep learning approaches. The results also revealed an increasing emphasis on multimodal data integration for emotion recognition and personalized learning support. Based on the findings, a conceptual model has been proposed to guide AIOL research and practice by integrating four components: data, AI processing, adaptive learning, and learner development. Overall, the study offered a data-driven overview of AIOL research and demonstrated how topic modeling can be used to examine thematic development in emerging research fields.

Author Biographies

Xieling Chen, School of Education, Guangzhou University, Guangzhou, China

Xieling Chen is an Associate Professor at Guangzhou University, China. Her research interests include artificial intelligence in education and text mining. She has over 100 publications. Stanford University has listed her as one of the World's Top 2% Scientists in 2022, 2023, 2024, and 2025.

Haoran Xie, School of Data Science, Lingnan University, Hong Kong SAR

Haoran Xie is a Professor at Lingnan University, Hong Kong. His research interests include artificial intelligence in education and big data. He has over 320 publications. He is the Editor-in-Chief/Associate Editor of several SCI/SSCI journals. Stanford University has listed him as one of the World's Top 2% Scientists in 2021, 2022, 2023, 2024, and 2025.

Xingquan Peng, School of Education, Guangzhou University, Guangzhou, China

Xingquan Peng is a Master Student at Guangzhou University, China. His research interests include data analysis and educational technology.

Xiaohui Tao, School of Mathematics, Physics, and Computing, University of Southern Queensland, Australia

Xiaohui Tao is the School Head, Acting Dean, and Professor of School of Mathematics, Physics, and Computing, University of Southern Queensland, Australia. His research interests include artificial intelligence and knowledge engineering.

Lin Li, School of Computer Science and Artificial Intelligence, Wuhan University of Technology, Wuhan, China

Lin Li is a Professor in the School of Computer Science and Artificial Intelligence at Wuhan University of Technology. Her research focuses on information retrieval, recommender systems, computational social science, and multimodal machine learning. She was recognized in the 2025 edition of the World's Top 2% Scientists list.

Joe Qin, School of Data Science, Lingnan University, Tuen Mun, Hong Kong SAR

Joe Qin is the Wai Kee Kau Chair Professor and President of Lingnan University, Hong Kong. His research interests include data science and analytics.

Fu Lee Wang, School of Science and Technology, Hong Kong Metropolitan University, Hong Kong SAR

Fu Lee Wang is the Dean and Professor at Hong Kong Metropolitan University, Hong Kong. His research interests include e-learning and information retrieval. Professor Wang has over 300 publications and 40 grants with more than 80 million Hong Kong dollars. He was also the Chair of ACM Hong Kong Chapter and IEEE Hong Kong Section Computer Society.

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Published

2026-08-07

How to Cite

Chen, X., Xie, H., Peng, X., Tao, X., Li, L., Qin, J., & Wang, F. L. (2026). Artificial Intelligence in Online Learning: Using BERTopic to Track Research Topics and Their Evolutions. The International Review of Research in Open and Distributed Learning, 27(3), 67–94. https://doi.org/10.19173/irrodl.v27i3.9516

Issue

Vol. 27 No. 3 (2026)

Section

Research Articles

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