The current research trend of artificial intelligence in language learning: A systematic empirical literature review from an activity theory perspective

Abstract

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.

A bibliometric analysis of artificial intelligence in L2 teaching and applied linguistics between 1995 and 2022

“…Additionally, clusters such as "gamification" and "computational linguistics" reflected a trend in exploring game-based approaches and competition in L2 teaching and learning research. Overall, these outstanding clusters demonstrate the increasing interest and potential of AI technologies in language learning and teaching, as supported by the relevant literature (Yang & Kyun, 2022). These ideas are further supported by the identification of the "artificial intelligence" cluster in Figure 3, which highlights the growing interest and potential of AI technologies in language learning and teaching, as well as its multidisciplinary nature.…”

“…Bibliometric studies by Huang, Hew and Fryer (2022) and Liang, Hwang, Chen and Darmawansah (2023) map the trends in AI applications in language education. However, as highlighted by Yang and Kyun (2022) and Ali (2020), further holistic syntheses are needed.…”

Transforming education through ICT: Exploring students' study practices in a resource‐constrained university setting

“…Lewin et al (2018) investigate how to change teachers' activity system—that is, the elements of the triangle—to scale up teachers' development of a digital pedagogy. Yang and Kyun (2022) use activity theory and the triangle model to grasp the data in a literature review on the use of artificial intelligence in language learning.…”

Heutagogical-based ICALL environment

“…In their research, Agonács and Matos (2019) highlighted some key findings related to published studies on heutagogy: the insufficient research on capability development and non-linearity dimension of heutagogy, the need of more longitudinal studies and quantitative statistical data to provide more empirical evidence, the limitation of geographic and cultural distribution of researchers as well as the population and sample size which mostly focus on adult learner population in formal educational context. In addition, Yang and Kyun (2022) also believe that the research that examined the practical role of teachers' intervention and configuration in ICALL context is scarce. In addition, Cochrane's et al (2022) study about the design for transformative mobile learning using the pedagogy-andragogy-heutagogy continuum revealed that there is still a great need to investigate how to design mobile learning contexts that develop students' epistemic understanding and reinitiation upon heutagogy.…”