Artificial Intelligence in Language Education: A Systematic Review of Trends and Measurable Learning Outcomes

Artificial intelligence (AI) is rapidly reshaping language education, making it increasingly important to evaluate its effectiveness through objectively measured learning outcomes rather than perceptions alone. This systematic review synthesizes empirical research published between 2023 and 2026 on the effects of AI-supported language learning. Studies were identified through online databases, with 280 studies meeting the eligibility criteria. The review examined trends in the evidence base and synthesized objectively measured improvements and null/negative effects. The results showed that publication activity increased significantly across the review period, while the literature was concentrated in higher education, English-language learning, productive skills, and generative AI-based applications. In addition, AI was associated with measurable improvements across writing, speaking, vocabulary, grammar, pronunciation, reading, listening, and general proficiency, particularly when it supported immediate feedback, repeated practice, revision, and adaptive interaction. However, effects were not uniform. Null and occasional negative outcomes emerged for particular subskills, delayed performance, and independent transfer, indicating that improved AI-assisted performance does not necessarily translate into sustained language development. The findings suggest that AI is most effective when embedded within structured pedagogy and combined with active learner engagement and teacher mediation. Its educational value thus depends less on technological novelty than on how effectively it supports durable, independent language development.