Navigating dual pathways: learning-oriented and instrumental identities in AI-mediated informal digital learning of English
TL;DR
This qualitative study examines the social identity categories and their construction processes among 12 Chinese university EFL learners engaged in AI-IDLE to demonstrate that AI-IDLE does not simply erase or replace existing social identities but selectively reinforces and reconfigures them through group norms, prototypical markers, and evaluative comparison.
Abstract
Generative AI is increasingly reshaping how learners engage in informal English learning beyond the classroom, making AI-mediated informal digital learning of English (AI-IDLE) an important emerging learning context. Yet research has predominantly focused on intra-personal psychological factors, the social dimensions of learning, particularly how learners construct their social identities in relation to AI, remain significantly underexplored. This qualitative study examines the social identity categories and their construction processes among 12 Chinese university EFL learners engaged in AI-IDLE. Drawing on Social Identity Theory (SIT), data from semi-structured interviews, reflective journals, and AI chat logs were analyzed using thematic analysis. The findings reveal two distinct identity pathways: learning-oriented academic identities, characterized by deep-learning commitment, humble mindset toward AI, and sustained immersion in the student role; and an instrumental identity, organized around a user category defined by transactional engagement, service satisfaction and surface learning approaches. Within this instrumental pathway, a more intensified fast-food consumer orientation emerged, which was explicitly self-identified by one participant and analytically inferred from behavioral patterns across several others. The study demonstrates that AI-IDLE does not simply erase or replace existing social identities but selectively reinforces and reconfigures them through group norms, prototypical markers, and evaluative comparison. These findings extend SIT by revealing how technological affordances and social configuration shape identity pathways that support or undermine meaningful learning engagement. Practical implications are proposed for AI developers, digital learning designers, educators, and curriculum designers to foster learning-oriented identity pathways in AI-mediated informal learning environments