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    <front>
        <journal-meta>
            <journal-title-group>
                <journal-title>Journal of Global Humanities and Social Sciences</journal-title>
            </journal-title-group>
            <issn media_type="print">2737-5374</issn>
            <issn media_type="electronic">2737-5382</issn>
            <publisher>
                <publisher-name>BONI FUTURE DIGITAL PUBLISHING CO.,LIMITED </publisher-name>
            </publisher>
            <url>https://ojs.bonfuturepress.com/index.php/GHSS/article/view/1954</url>
            <volume>6</volume>
            <issue>7</issue>
            <year>2025</year>
            <published-time>2025-12-25</published-time>
            <title>Reconfiguring Learner Agency in AI-Mediated Virtual Language Learning: A Three-Dimensional Action-Chain Model</title>
            <author>Jun Zou,Jinyang Zhao</author>
            <abstract>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.</abstract>
            <keywords>learner agency,artificial intelligence (AI),virtual learning environments,mediated discourse analysis,algorithmicity</keywords>
        </journal-meta>
        <article-meta>
            <article-id pub-id-type="doi">10.61360/BoniGHSS252019540714</article-id>
        </article-meta>
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