Despite growing enthusiasm around generative AI (GenAI) as a transformative force in education, access to these technologies remains profoundly unequal. This qualitative study examines how educators and students in under‐resourced Chinese educational contexts experience unequal access to GenAI and interpret its consequences for language learning, teaching and local knowledge production. Informed by critical sociolinguistics and political economy, the study draws on ten semi‐structured interviews with six teachers/educators and four students from local universities, rural communities and linguistically minoritised settings in China. The analysis shows that GenAI access is rarely a binary matter of presence or absence, but a layered set of hurdles that participants had to navigate actively: tools are visible through media, training sessions and peer networks, but unstable connectivity, outdated devices, paywalls, institutional permissions and limited local integration meant that turning visibility into sustained use required ongoing effort, workaround strategies and situated forms of agency. Participants also reported English‐centred performance, weak support for local dialects and minority languages and culturally generic or stereotyped outputs. Although empirically situated in China, the study uses this contextually bounded dataset to contribute analytically to wider debates on global digital inequality, linguistic hierarchy and algorithmic colonialingualism.
Jinming Du, Qinghua Chen, Wei Wei· International Journal of App...· 0 citations
Generative AI (GenAI) has transformed L2 writing, producing human-like prose but often impersonal feedback. This study explores the potential of GenAI–human collaborative feedback, focusing on the first author’s experience as a teaching assistant in a Hong Kong public university’s Bachelor of Education (English language track). Grounded in Ecological Languaging Competencies (ELC) and its affordance framework, this study employs an ethnographic approach informed by narrative inquiry and phenomenology. Data were drawn from Zoom tutoring sessions incorporating interview-style questions to investigate participants’ perspectives and experiences, GenAI-student conversation logs, and final assignments in order to analyze two multilingual students’ GenAI–human-mediated L2 writing processes. Findings are organized around three ELC-informed themes: (1) whole-body sense-making and the meshing of first-order languaging and second-order language; (2) individual languaging agency within a distributed ecosystem; and (3) environmental affordances and functional fit. In both cases, GenAI demonstrates consistent limitations in facilitating the situated, embodied, and affectively attuned dimensions of languaging that effective L2 writing entails. This study makes two contributions: it extends ELC’s affordance network to tertiary-level GenAI–human-mediated L2 writing, and it reconceptualizes writerly authorship as a distributed yet agentively orchestrated practice. Moreover, co-agentic GenAI–human feedback foregrounds ecological embeddedness, writerly agency, and ethical GenAI integration.
Lu Xi, Qinghua Chen, A. M. Lin· Education sciences· 0 citations
