Roles and research foci of artificial intelligence in language education: an integrated bibliographic analysis and systematic review approach
By Jiaen Liang, Gwo-Jen Hwang, Mei-Rong Alice Chen, Darmawansah Darmawansah. Interactive Learning Environments, 2021
328 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Computer Science Applications
View Paper: https://www.semanticscholar.org/paper/695bfb7a3c6c1b29c189739b7556c59a8dc4f515
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Citations in the corpus
- Teachers’ AI digital competencies and twenty-first century skills in the post-pandemic world (2023, methodology): “AI technologies have been used in applications for learning in almost every discipline such as language and arts (Liang et al., 2021), and STEM related areas (Zawacki-Richter et al., 2019).”
- Factors Influencing University Students’ Behavioral Intention to Use Generative Artificial Intelligence: Integrating the Theory of Planned Behavior and AI Literacy (2024, neutral): “…strides in various specific educational domains, including engineering (Khan et al., 2024), language (Cai et al., 2023; Chen et al., 2023; Liang et al., 2023), programming (Jing et al., 2024; Yilmaz & Yilmaz, 2023b), online learning (Hwang et al., 2021), intelligent learning (Chen et…”
- The metaverse in education: Definition, framework, features, potential applications, challenges, and future research topics (2022, background): “However, for several reasons, such as lack of contextual practice or interaction, traditional language learning has been taken passively either in the classroom or out of class (Liang et al., 2021).”