This article elaborates a theoretical framework for enhancing English Language Teaching (ELT) methodology through the principled use of artificial intelligence (AI) tools. Synthesizing perspectives from sociocultural theory, complex dynamic systems, cognitive apprenticeship, and learning analytics, it argues that AI’s pedagogical value lies not in automation alone but in the augmentation of formative feedback, the orchestration of adaptive tasks, and the expansion of learners’ opportunities for authentic communication and metacognitive regulation. The paper reconceptualizes AI in ELT as a set of mediational affordances that can diagnose, scaffold, and extend learner performance across reading, writing, listening, speaking, vocabulary, and grammar, while foregrounding ethical and ecological considerations such as bias, privacy, and teacher agency. Materials and methods consist of a narrative synthesis of relevant literature and a design-principles analysis that maps theoretical constructs to functional AI capabilities, including large language models, intelligent tutoring systems, automated speech recognition, probabilistic recommendation engines, and analytics dashboards. Results are presented as a coherent set of theoretically grounded design principles: align AI feedback with learning-oriented assessment; couple adaptivity with transparency and learner control; embed AI as a dialogic partner that elicits hypothesis formation and repair; and institute teacher-facing analytics that support just-in-time pedagogy rather than high-stakes surveillance. The conclusion highlights implications for curriculum, teacher education, and policy, proposing an “augmented pedagogy” model in which AI expands but does not replace the human pedagogical core, and outlines directions for future research on validity, fairness, and transfer. 

Artificial intelligence, ELT methodology, learning analytics, large language models

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