APA
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Wang, C., Sun, L., Yan, J., & Zou*, B. (2026). Disciplinary and educational level differences in AI-mediated informal digital Learning of English (AI-IDLE): A qualitative epistemic network analysis. Journal of Computer Assisted Learning, 42(3), e70244. https://doi.org/10.1002/jcal.70244
Chicago/Turabian
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Wang, Chenghao, Lanfang Sun, Jiahao Yan, and Bin Zou*. “Disciplinary and Educational Level Differences in AI-Mediated Informal Digital Learning of English (AI-IDLE): A Qualitative Epistemic Network Analysis.” Journal of Computer Assisted Learning 42, no. 3 (2026): e70244.
MLA
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Wang, Chenghao, et al. “Disciplinary and Educational Level Differences in AI-Mediated Informal Digital Learning of English (AI-IDLE): A Qualitative Epistemic Network Analysis.” Journal of Computer Assisted Learning, vol. 42, no. 3, 2026, p. e70244, doi:10.1002/jcal.70244.
BibTeX Click to copy
@article{chenghao2026a,
title = {Disciplinary and educational level differences in AI-mediated informal digital Learning of English (AI-IDLE): A qualitative epistemic network analysis},
year = {2026},
issue = {3},
journal = {Journal of Computer Assisted Learning},
pages = {e70244},
volume = {42},
doi = {10.1002/jcal.70244},
author = {Wang, Chenghao and Sun, Lanfang and Yan, Jiahao and Zou*, Bin}
}
Background: As generative artificial intelligence (GenAI) becomes more deeply integrated into AI-mediated informal digital learning of English (AI-IDLE), understanding how learners organise their acceptance of these tools is increasingly important. Existing research has largely relied on variable-centred approaches, offering limited insight into how acceptance beliefs are configured across learner groups.
Objectives: This study examines how learners' acceptance of GenAI beyond the classroom is structurally organised and how these configurations vary across educational levels and disciplinary backgrounds.
Methods: Grounded in the Integrated Model of Technology Acceptance (IMTA), the study employed Epistemic Network Analysis (ENA) to model four acceptance networks: overall IMTA, perceived enjoyment (PE), perceived usefulness (PU) and negative use experience. Semi-structured online interviews were conducted with 24 Chinese university students (BA, MA, PhD; humanities and social sciences, STEM) and theory-driven coding was used to construct and compare network structures.
Results and Conclusions: Findings revealed a developmental reconfiguration of acceptance. BA learners' IMTA networks were experience-oriented (PE, PEU), whereas postgraduate learners showed more utility-driven configurations integrating PU and behavioural intention. PE networks showed disciplinary differences and some developmental variation, shifting from accompaniment-centred structures towards confidence-oriented patterns. PU displayed the clearest educational differentiation, progressing from affordance-based evaluations to goal-aligned and critically engaged use. In contrast, negative-use networks showed structural stability across educational levels but differed by discipline. Overall, GenAI acceptance in AI-IDLE emerges as a developmentally structured and motivationally layered process rather than a static set of beliefs.