Engagement Dynamics in AI‐Mediated Informal Digital Learning of English: Effects on L2 Speaking Performance, Anxiety, and Sequential Behaviour Patterns.
Background: In Informal digital learning of English (IDLE) environments, recent advances in generative artificial intelligence (AI) have created new opportunities for self‐directed second language (L2) speaking practise through responsive and low‐stakes interaction. However, AI‐mediated IDLE (AI‐IDL...
| Publicado en: | Journal of Computer Assisted Learning Vol. 42; no. 4; pp. 1 - 19 |
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| Autores principales: | , , , , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
Aug2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=195655206&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 195655206 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 02664909 6M1 jtl: Journal of Computer Assisted Learning issn: 02664909 maglogo: Y pubinfo: dt: Aug2026 vid: 42 iid: 4 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 195655206 195655206 195655206 10.1002/jcal.70278 195655206 ppf: 1 ppct: 18 formats: tig: atl: Engagement Dynamics in AI‐Mediated Informal Digital Learning of English: Effects on L2 Speaking Performance, Anxiety, and Sequential Behaviour Patterns. aug: au: Chen, Yuting Ma, Qing Jong, Morris Siu‐Yung Yang, Youlin Cukurova, Mutlu Li, Ming affil: Centre for Learning Sciences and Technologies, The Chinese University of Hong Kong, Hong Kong SAR,, China sug: subj: Artificial Intelligence Learning Methods English as a Second Language Education Language Processing Anxiety Psychosocial Factors Educational Technology Students, College Psychosocial Factors Human Funding Source Male Female Young Adult One-Way Analysis of Variance Speech Rate Educational Measurement Voice Recognition Systems Avatars Feedback Motivation Self-Directed Learning User-Computer Interface Structural Equation Modeling China Computer-Assisted Instruction Learning Environment Self Report Power Analysis Nonparametric Statistics Wilcoxon Signed Rank Test Questionnaires Cognition Emotions Grammar Kruskal-Wallis Test Post Hoc Analysis Descriptive Statistics Data Analysis Software Male Female ab: Background: In Informal digital learning of English (IDLE) environments, recent advances in generative artificial intelligence (AI) have created new opportunities for self‐directed second language (L2) speaking practise through responsive and low‐stakes interaction. However, AI‐mediated IDLE (AI‐IDLE) is not experienced uniformly: learners differ in how they engage with AI, and such differences may be reflected not only in speaking performance and foreign language speaking anxiety (FLSA) but also in the sequential behaviours through which engagement is enacted. Objectives: This study aimed to investigate how different profiles of learner engagement in AI‐IDLE are associated with L2 speaking performance, FLSA, and sequential behaviour patterns during AI‐learner interaction. Methods: An AI‐IDLE environment was developed to provide scaffolded, avatar‐based speaking practise in contextualised scenarios. Sixty university L2 learners participated in this study. Latent profile analysis (LPA) was conducted based on self‐reported engagement measures to identify distinct engagement profiles. Differences in speaking performance and FLSA were compared across profiles, and sequential behaviour analysis was employed to examine enacted engagement as reflected in AI‐learner interaction sequences. Results and Conclusion: Three engagement profiles were identified. Differences across profiles were observed in both speaking performance and FLSA. Sequential analysis further revealed that learners exhibited distinct patterns of interaction with AI, characterised by varying degrees of iteration and continuity in behaviour sequences. These findings suggest that distinct engagement profiles are associated not only with different learning outcomes but also with qualitatively different patterns of interaction with AI. Implications for Practise: The study contributes to AI‐IDLE research by integrating person‐centred and process‐oriented perspectives, highlighting the importance of examining both perceived and enacted engagement. The findings offer implications for the design of AI‐IDLE that support sustained interaction, adaptive feedback use, and reduced FLSA. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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