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Although the field of artificial intelligence (AI) has rapidly developed, there has been little research to review, describe, and analyse the trends and development of empirical research on AI-supported language learning. This paper selected and analysed 25 empirical research papers on AI-supported language learning published in the last 15 years. These empirical studies were analysed using the activity theory from seven constituents: tool, subject, object, rules, community, division of labour, and outcome. A key contribution of this paper is the use of activity theory to illustrate the dynamic interactions and contradictions between the seven elements. AI-supported technology as a mediating tool demonstrated some effectiveness in language learning but needs further improvement in the use of language for communication and collaborative design. We argue that teachers’ intervention and configuration of AI-supported language learning in the pedagogical design plays an important role in the effectiveness of learning. More research is needed to explore the use of AI-supported language learning in the classroom or the real-life learning context. Implications for practice or policy: Research on AI-supported language learning should view teacher and students as active agents in interacting with technology and making transformations in real life learning situations. More research should focus on productive dialogue and communication in AI-supported language learning with collaborative design. A mixed module of AI-supported language learning and formal teacher instruction should be incorporated in pedagogical design.
Yang et al. (Sun,) studied this question.