Synthesizing AI-enhanced language learning through the lens of complex dynamic systems theory: a systematic literature review

Complex Dynamic Systems Theory (CDST) offers a valuable framework for understanding language learning as a non-linear, adaptive trajectory, reflecting the growing influence of Artificial Intelligence (AI) on the educational ecosystem. This systematic literature review synthesizes 35 empirical studies to investigate AI integration in English as a Foreign Language (EFL) education. Findings suggest that AI-mediated learning reflects several CDST hallmarks, including co-adaptation, non-linearity, and systemic interconnectedness. Specifically, AI technologies appear to function as dynamic influences that may disrupt established learning patterns, potentially facilitating transitions toward more advanced proficiency. As a key environmental factor, AI appears to reshape the learning landscape by providing ecological affordances – such as real-time scaffolding, adaptive tutoring, and personalized feedback loops – across both formal and informal contexts. Furthermore, AI-driven interactions appear to be associated with shifts in teacher roles and may catalyze identity negotiation among learners through continuous feedback cycles. Methodologically, this review employs BERTopic modeling during the screening phase to ensure thematic rigor. A key feature of this review is the meta-level reinterpretation of empirical research that, while not always explicitly grounded in CDST, utilizes methodological designs that reflect core features of complex systems. By synthesizing these findings, the review identifies competitive and synergistic couplings between learners, technologies, and tasks that have received limited attention in prior literature. Finally, this review calls for greater attention to the emergent and temporal dynamics of the learning process in future EFL research, moving beyond a sole focus on static learning outcomes.