Roles and research foci of artificial intelligence in language education: an integrated bibliographic analysis and systematic review approach

ABSTRACT

This study explores the roles and research foci of AILEd (Artificial Intelligence in Language Education). The AILEd studies published from 1990 to 2020 in the WOS (Web of Science) database were included in the present study. Based on the well-recognized Technology-based Learning Review model, several dimensions, such as research methods, research sample groups, adopted technology, language skills, the role of AI in language education, and learning outcomes, were taken into account. The review results show that the main application domains of AILEd research were writing, reading, and vocabulary acquisition. In terms of applied technology and algorithms, AI in language education mostly adopted ITS (Intelligent Tutoring System) and NLP (Natural Language Processing). Besides, several commonly used AI algorithms were Statistical Learning, Data Mining, Machine Learning, and Natural Language Parsing. It was also found that some research focused on learning anxiety, willingness to communicate, knowledge acquisition, and classroom interaction. However, higher order thinking, complex problem solving, critical thinking ability, and collaborative learning tendencies were rarely considered in AILEd studies. Accordingly, several suggestions are provided to researchers, teachers, and decision makers for applying or studying AI applications in language education in the future.

KEYWORDS:

• Artificial intelligence
• language learning
• literature review
• research trends

Acknowledgements

This study is supported in part by the Ministry of Science and Technology of Taiwan under contract numbers MOST-109-2511-H-011-002-MY3 and MOST-108-2511-H-011-005-MY3.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This study is supported in part by the Ministry of Science and Technology of Taiwan under [contract numbers MOST-109-2511-H-011-002-MY3 and MOST-108-2511-H-011-005-MY3].

Notes on contributors

Jia-Cing Liang

Mr. Jia-Cing Liang is a Ph.D. student at the Graduate Institute of Digital Learning and Education. National Taiwan University of Science and Technology. His research interests include technology in language learning, computer-assisted language learning, and pedagogy and learning design.

Gwo-Jen Hwang

Professor. Gwo-Jen Hwang is a chair professor at the Graduate Institute of Digital Learning and Education, National Taiwan University of Science and Technology, Taiwan. His research interests include computer supported social networking, personalized and adaptive learning environments, mobile and embedded learning analytics, educational data mining and educational data science.

Mei-Rong Alice Chen

Dr. Mei-Rong Alice Chen is a post-doctoral researcher at the Graduate Institute of Digital Learning and Education, National Taiwan University of Science and Technology, Taiwan. Her research interests include on technology-enhanced language learning (TELL), learning technologies, flipped learning, and computer-assisted language learning (CALL), learning analytics, innovative pedagogy design.

Darmawansah Darmawansah

Mr. Darmawansah is a Ph.D. student at the Graduate Institute of Digital Learning and Education. National Taiwan University of Science and Technology. His research interests include gamified learning, technology-enhanced learning, and visual argumentation.