Effectiveness of Artificial Intelligence (AI) in language teaching

This study examines the effectiveness of artificial intelligence (AI) in language teaching, particularly in English as a Foreign Language (EFL) classrooms, following AI's increased adoption after the COVID-19 pandemic. Through a multilevel meta-analysis of 117 effect sizes across 46 empirical studies published between 2022 and 2025, results show that AI has a statistically significant medium-to-large overall impact on language learning (g = 0.74, 95 % CI [0.57, 0.92], p < .001) across all five major skills, with vocabulary showing the strongest effects, followed by reading, writing, listening, and speaking. Grounded in constructivist, adaptive learning, and cognitive load theories, moderator analyses revealed several key insights: (1) AI is more effective in face-to-face and blended settings than in fully online classrooms; (2) AI is particularly effective for younger K-12 learners, suggesting tools are pedagogically optimized for foundational language learning; (3) similar effectiveness outcomes across AI platforms suggest implementation matters more than the tool itself; (4) AI can facilitate task completion but not develop long-term autonomous learning habits like self-regulation; and (5) AI works best as a supplement rather than replacement for traditional teaching. The results, when interpreted through the novelty effect, cognitive load theory, and attention economy frameworks, suggest a technology saturation effect: the failure of AI tools to capture distinctive attention, reduce cognitive burden, or secure focused engagement in an already technology-rich environment. The synthesis outlines the current state of EFL literature, which has been focused on Asia and the Middle East, offering practical insights for educators considering AI integration.