Artificial intelligence in English as a Foreign Language Learning: a comprehensive theoretical framework for pedagogical integration and psychological well-being

This theoretical paper presents a conceptual framework for understanding the integration of artificial intelligence (AI) in English as a Foreign Language (EFL) learning contexts. Drawing on established foreign language acquisition theories, sociocultural learning paradigms, and emerging digital pedagogy scholarship, the paper develops the AI-Enhanced Foreign Language Learning (AEFLL) framework around a specific theoretical proposition. AI tools, unlike the human interlocutors around which classical SLA theory was built, can decouple the cognitive, affective, and social functions that face-to-face instruction has traditionally delivered as a single, integrated package. This decoupling, rather than the broader observation that cognition, affect, and social interaction all matter for language learning, constitutes the framework’s central theoretical contribution. It generates directional, testable hypotheses about how different AI tools, by recombining these functions in different proportions, produce different developmental trade-offs for EFL learners. The paper situates this argument within current scholarship on human-AI collaboration, critical AI literacy, and the sociopolitical risks of AI-mediated education, including linguistic inequality and data colonialism, and offers a balanced account of the psychological benefits and risks associated with AI-mediated learning. Practical implications for curriculum design and teacher professional development are discussed alongside a structured agenda for empirical validation.