Key points are not available for this paper at this time.
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.
Liang et al. (2021) studied this question.