What Predicts (AI‐Mediated) Informal Digital Learning of English in the Global South? The Case of Rural Bangladeshi Students.
Background: Amid Bangladesh's national efforts to enhance digital inclusion, rural university students continue to face persistent infrastructural and educational inequalities that limit their access to quality English learning opportunities. While informal digital environments increasingly support...
| Publicado en: | Journal of Computer Assisted Learning Vol. 42; no. 2; pp. 1 - 15 |
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| Autores principales: | , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
Apr2026
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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=192476902&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 192476902 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 02664909 6M1 jtl: Journal of Computer Assisted Learning issn: 02664909 maglogo: Y pubinfo: dt: Apr2026 vid: 42 iid: 2 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 192476902 192476902 192476902 10.1002/jcal.70196 192476902 ppf: 1 ppct: 14 formats: tig: atl: What Predicts (AI‐Mediated) Informal Digital Learning of English in the Global South? The Case of Rural Bangladeshi Students. aug: au: Liu, Guangxiang Leon Hossain, Md Kamal affil: School of Foreign Languages, Southeast University, Nanjing, China sug: subj: Motivation Evaluation Learning English Language Online Education Artificial Intelligence Students, College Psychosocial Factors Students, Undergraduate Psychosocial Factors Bangladeshi Persons Rural Population Bangladesh Human Male Female Young Adult Quantitative Studies Cross Sectional Studies Convenience Sample Questionnaires Scales Coefficient alpha Criterion-Related Validity Discriminant Validity Pearson's Correlation Coefficient Regression P-Value Data Analysis Software Descriptive Statistics Self Report Self-Efficacy Computer-Assisted Instruction Affect Male Female ab: Background: Amid Bangladesh's national efforts to enhance digital inclusion, rural university students continue to face persistent infrastructural and educational inequalities that limit their access to quality English learning opportunities. While informal digital environments increasingly support learners' out‐of‐class English development, little is known about what factors predict rural learners' participation in such practices, especially as artificial intelligence (AI) tools reshape the digital learning ecology. Addressing this gap is critical for ensuring that digital learning benefits learners in resource‐limited contexts. Objectives: Guided by the proactive language learning theory, this study aims to identify the sociocontextual, motivational and affective factors that predict rural Bangladeshi students' involvement with informal digital learning of English (IDLE) and its emerging form, AI‐mediated IDLE (AI‐IDLE). Methods: We collected data from 508 undergraduate students from rural Bangladesh using an online survey. Hierarchical regression analyses were conducted to examine the extent to which sociobiographical, sociotechnical, motivational and affective variables predict IDLE and AI‐IDLE. Results and Conclusions: Self‐efficacy, enjoyment and the ideal L2 self significantly predicted IDLE, while AI‐IDLE was positively predicted by IDLE and the ought‐to L2 self but negatively predicted by the ideal L2 self, with university type also showing a significant effect. These findings highlight that learners' affective and motivational dispositions, rather than demographic or sociotechnical factors, are central to shaping informal English learning with technology in underrepresented contexts. The study also underscores that prior informal learning experience provides a foundation for AI tool adoption for out‐of‐class learning purposes and advances an inclusive understanding of how Global South learners engage proactively in evolving digital learning ecologies. Practitioner Note: What is currently known about this topic? ○Informal digital learning of English (IDLE) supports autonomous L2 learning○Learners' motivation and emotions are key to sustaining informal engagement.○AI tools are reshaping how learners access and practice English beyond class.What does this paper add? ○Examines IDLE and AI‐IDLE among rural university students in Bangladesh.○Shows that self‐efficacy, enjoyment and motivation drive informal learning the most.○Reveals that prior IDLE experience predicts learners' AI‐IDLE.Implications for practice or policy ○Build learners' confidence through scaffolded digital and AI learning tasks.○Use enjoyable, locally relevant activities to sustain engagement and effort.○Connect AI use with learners' aspirational identities and authentic English use. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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