Dynamic Contingency in AI-Supported EFL Speaking: A Literature-Based Analysis
DOI:
https://doi.org/10.24090/120p0s18Keywords:
AI in Language Learning, EFL Speaking; Dynamic Contingency, Complex Dynamic Systems Theory (CDST), Literature-Based AnalysisAbstract
This study examines dynamic contingency in AI-supported English as a Foreign Language (EFL) speaking by synthesizing findings from international research literature. While previous studies have widely explored the effectiveness of artificial intelligence (AI) in enhancing speaking skills, limited attention has been given to understanding learning as a dynamic, context-dependent process. Drawing on a literature-based analytical approach, this study reviews selected peer-reviewed articles focusing on AI applications such as chatbots, automatic speech recognition (ASR), and adaptive learning systems in EFL speaking contexts. The analysis reveals that AI-mediated speaking development reflects non-linear and context-sensitive patterns, influenced by factors such as learner characteristics, feedback mechanisms, and task conditions. The reviewed studies indicate variability in learners’ performance and engagement, highlighting the role of adaptive interactions and continuous feedback. This study contributes to the field by reframing AI-supported EFL speaking through the lens of dynamic contingency, emphasizing variability, adaptability, and context sensitivity in language development. It further suggests the need for pedagogical approaches that accommodate diverse and evolving learner trajectories in AI-enhanced environments.
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