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Engagement Dynamics in AI-Mediated Informal Digital Learning of English: Effects on L2 Speaking Performance, Anxiety, and Sequential Behaviour Patterns

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Τίτλος: Engagement Dynamics in AI-Mediated Informal Digital Learning of English: Effects on L2 Speaking Performance, Anxiety, and Sequential Behaviour Patterns
Γλώσσα: English
Συγγραφείς: Yuting Chen (ORCID 0009-0000-9300-4606), Qing Ma (ORCID 0000-0003-3125-3513), Morris Siu-Yung Jong (ORCID 0000-0001-9186-0484), Youlin Yang (ORCID 0009-0001-5025-7192), Mutlu Cukurova (ORCID 0000-0001-5843-4854), Ming Li (ORCID 0000-0002-1218-2804)
Πηγή: Journal of Computer Assisted Learning. 2026 42(4).
Διαθεσιμότητα: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
Peer Reviewed: Y
Page Count: 18
Ημερομηνία έκδοσης: 2026
Τύπος εγγράφου: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: English (Second Language), Artificial Intelligence, Informal Education, Anxiety, Oral Language, Speech Communication, Second Language Learning, College Students, Profiles, Language Patterns, Technology Uses in Education
DOI: 10.1002/jcal.70278
ISSN: 0266-4909
1365-2729
Περίληψη: 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.
Abstractor: As Provided
Entry Date: 2026
Αριθμός Καταχώρησης: EJ1513481
Βάση Δεδομένων: ERIC
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  Data: Engagement Dynamics in AI-Mediated Informal Digital Learning of English: Effects on L2 Speaking Performance, Anxiety, and Sequential Behaviour Patterns
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  Data: <searchLink fieldCode="AR" term="%22Yuting+Chen%22">Yuting Chen</searchLink> (ORCID <externalLink term="https://orcid.org/0009-0000-9300-4606">0009-0000-9300-4606</externalLink>)<br /><searchLink fieldCode="AR" term="%22Qing+Ma%22">Qing Ma</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-3125-3513">0000-0003-3125-3513</externalLink>)<br /><searchLink fieldCode="AR" term="%22Morris+Siu-Yung+Jong%22">Morris Siu-Yung Jong</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-9186-0484">0000-0001-9186-0484</externalLink>)<br /><searchLink fieldCode="AR" term="%22Youlin+Yang%22">Youlin Yang</searchLink> (ORCID <externalLink term="https://orcid.org/0009-0001-5025-7192">0009-0001-5025-7192</externalLink>)<br /><searchLink fieldCode="AR" term="%22Mutlu+Cukurova%22">Mutlu Cukurova</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-5843-4854">0000-0001-5843-4854</externalLink>)<br /><searchLink fieldCode="AR" term="%22Ming+Li%22">Ming Li</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-1218-2804">0000-0002-1218-2804</externalLink>)
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  Data: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
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  Data: 10.1002/jcal.70278
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  Data: 0266-4909<br />1365-2729
– Name: Abstract
  Label: Abstract
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  Data: 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.
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        Value: 10.1002/jcal.70278
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      – Text: English
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        PageCount: 18
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      – SubjectFull: English (Second Language)
        Type: general
      – SubjectFull: Artificial Intelligence
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      – SubjectFull: Informal Education
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      – SubjectFull: Language Patterns
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      – TitleFull: Engagement Dynamics in AI-Mediated Informal Digital Learning of English: Effects on L2 Speaking Performance, Anxiety, and Sequential Behaviour Patterns
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