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Artificial intelligence (AI) is reshaping foreign language education by enabling personalized feedback, real-time assistance, and scalable interaction. However, existing reviews have seldom anchored these developments in a coherent theory of learning. Framed by Connectivism, this study synthesizes recent empirical evidence to examine how AI tools, particularly large-language-model chatbots and adaptive systems, support four core dimensions of networked learning: network formation, information access and sharing, adaptive learning, and learner autonomy. The synthesis indicates that AI not only augments access and personalization but also reconfigures learning networks by introducing an active ‘AI node’ that routes, generates, and curates knowledge. This reconfiguration strengthens weak ties, accelerates feedback cycles, and expands learners’ opportunities for participation across contexts. Implications include network-oriented instructional design, assessment practices aligned with autonomy, and safeguards for privacy and transparency. The review highlights the need for longitudinal and cross-context studies that evaluate the scalability and ethics of AI-mediated networked learning. Overall, situating AI within a Connectivist lens clarifies both its pedagogical promise and the conditions under which it can reliably enhance language learning.
Li et al. (Thu,) studied this question.