Reconfiguring Learner Agency in AI-Mediated Virtual Language Learning: A Three-Dimensional Action-Chain Model (Journal of Global Humanities and Social Sciences, 2025)
Artificial intelligence (AI)–enhanced virtual environments are increasingly used for foreign language learning, yet their impact on learner agency—the capacity to perceive, initiate, and regulate action—remains under-theorized. This study aims to explain how learner agency is reconfigured in AI-mediated virtual language learning environments and to develop an action-chain model that captures this reconfiguration.Drawing on mediated discourse analysis (MDA) and theories of distributed agency and sociotechnical systems, the study proposes a three-dimensional theoretical framework of mediatedness, agency, and algorithmicity. Qualitative data were collected from interaction logs, system traces, and semi-structured interviews with 32 Chinese EFL university learners using an AI-supported virtual learning platform. The data were analyzed through MDA-informed action-chain analysis and thematic coding.The findings reveal a three-stage developmental pattern of learner agency: agency compression, where algorithmic pre-structuring narrows action possibilities; agency distribution, where human–AI co-action becomes the dominant mode of participation; and agency regeneration, where learners reassert strategic control and use AI as a resource rather than an authority. These stages are shaped by the interplay of multimodal mediatedness, algorithmic structuring, and agentive adaptation.Learner agency in AI-mediated virtual environments is a dynamic sociotechnical phenomenon rather than a stable individual trait. The proposed action-chain model offers a theoretical lens for understanding this reconfiguration and provides implications for designing AI-supported learning ecologies that foster, rather than constrain, learner agency.