A skill-specific perspective on AI-mediated informal digital learning of English (AI-IDLE): Examining the contributing roles of L2 writing motivation and enjoyment

Artificial intelligence-mediated informal digital learning of English (AI-IDLE) is emerging as a promising field in computer-assisted language learning (CALL). This study asserts a need to adopt a skill-specific (e.g., writing) perspective to examine the unique challenges and psychological mechanisms associated with second language (L2) writing. Drawing upon the theoretical concepts of L2 writing future selves (i.e., the ideal L2 writing self and the ought-to L2 writing self) and L2 writing enjoyment, this study seeks to investigate the motivational and affective basis of AI-IDLE writing (AI-IDLE-W). We collected questionnaire data from 412 Chinese university students and conducted postsurvey interviews with 12 of them. The quantitative findings demonstrated that both the ideal L2 writing self and the ought-to L2 writing self can significantly influence AI-IDLE-W, but only the ideal L2 writing self can have an effect on students’ L2 writing enjoyment. Analysis of the interview data not only confirmed quantitative findings but also added nuances and contextual details to them. Furthermore, it disclosed a more dynamic and complicated mechanism of how L2 writing future selves, enjoyment, and AI-IDLE-W intertwine with each other as learners negotiate their own learning contexts. This explanatory sequential mixed-method design reveals implications for future pedagogical practices in supporting L2 learners’ writing skill development in the environment of AI-IDLE.

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Publication Details

Journal
Language Teaching Research
Published
2025-10-14
DOI
https://doi.org/10.1177/13621688251375547
Citations
7
Primary Topic
Online Learning and Analytics
Type
article
Field-Weighted Citation Impact
11.86
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article

A skill-specific perspective on AI-mediated informal digital learning of English (AI-IDLE): Examining the contributing roles of L2 writing motivation and enjoyment

Mark Feng Teng, Huiqing Chen, Ali Soyoof, Minlin Zou
7 citations
Language Teaching Research
Online Learning and Analytics
11.86
article

A skill-specific perspective on AI-mediated informal digital learning of English (AI-IDLE): Examining the contributing roles of L2 writing motivation and enjoyment

Mark Feng Teng, Huiqing Chen, Ali Soyoof, Minlin Zou
article en
7 citations

Abstract

Artificial intelligence-mediated informal digital learning of English (AI-IDLE) is emerging as a promising field in computer-assisted language learning (CALL). This study asserts a need to adopt a skill-specific (e.g., writing) perspective to examine the unique challenges and psychological mechanisms associated with second language (L2) writing. Drawing upon the theoretical concepts of L2 writing future selves (i.e., the ideal L2 writing self and the ought-to L2 writing self) and L2 writing enjoyment, this study seeks to investigate the motivational and affective basis of AI-IDLE writing (AI-IDLE-W). We collected questionnaire data from 412 Chinese university students and conducted postsurvey interviews with 12 of them. The quantitative findings demonstrated that both the ideal L2 writing self and the ought-to L2 writing self can significantly influence AI-IDLE-W, but only the ideal L2 writing self can have an effect on students’ L2 writing enjoyment. Analysis of the interview data not only confirmed quantitative findings but also added nuances and contextual details to them. Furthermore, it disclosed a more dynamic and complicated mechanism of how L2 writing future selves, enjoyment, and AI-IDLE-W intertwine with each other as learners negotiate their own learning contexts. This explanatory sequential mixed-method design reveals implications for future pedagogical practices in supporting L2 learners’ writing skill development in the environment of AI-IDLE.

Language Teaching Research
Hunan University of Science and Technology (CN), University of Exeter (GB), Education University of Hong Kong (HK), Macao Polytechnic University (MO)
Openalex Percentile: Top 2%
Online Learning and Analytics
11.86
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