Disciplinary and Educational Level Differences in AI-Mediated Informal Digital Learning of English (AI-IDLE): A Qualitative Epistemic Network Analysis
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| Title: | Disciplinary and Educational Level Differences in AI-Mediated Informal Digital Learning of English (AI-IDLE): A Qualitative Epistemic Network Analysis |
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| Language: | English |
| Authors: | Chenghao Wang (ORCID |
| Source: | Journal of Computer Assisted Learning. 2026 42(3). |
| Availability: | 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 |
| Publication Date: | 2026 |
| Document Type: | Journal Articles Reports - Research |
| Education Level: | Higher Education Postsecondary Education |
| Descriptors: | Artificial Intelligence, Electronic Learning, English (Second Language), Student Attitudes, Undergraduate Students, Graduate Students, Doctoral Students, Intellectual Disciplines, Grade Level Differences, Foreign Countries, Informal Education |
| Geographic Terms: | China |
| DOI: | 10.1002/jcal.70244 |
| ISSN: | 0266-4909 1365-2729 |
| Abstract: | 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. |
| Abstractor: | As Provided |
| Entry Date: | 2026 |
| Accession Number: | EJ1506865 |
| Database: | ERIC |
| Abstract: | 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. |
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| ISSN: | 0266-4909 1365-2729 |
| DOI: | 10.1002/jcal.70244 |
