["Journal of Computer Assisted Learning, Volume 42, Issue 3, June 2026. ", "\nABSTRACT\n\nBackground\nAs 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.\n\n\nObjectives\nThis study examines how learners' acceptance of GenAI beyond the classroom is structurally organised and how these configurations vary across educational levels and disciplinary backgrounds.\n\n\nMethods\nGrounded 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.\n\n\nResults and Conclusions\nFindings 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.\n\n"]