Artificial intelligence-assisted vocabulary teaching: a meta-analysis study

Introduction: The literature reports promising findings on the use of artificial intelligence (AI) to enhance vocabulary learning performance. However, this body of research is still relatively recent, important gaps remain, and findings vary across studies. Therefore, synthesizing the available evidence is essential to reach more generalizable conclusions and to gain deeper insight into AI-supported vocabulary instruction. Methods: This study employed a meta-analytic design to examine the effects of AI-supported interventions on individuals' vocabulary learning performance. Articles published between 2015 and 2025 were retrieved from the Web of Science Core Collection (WOS), Scopus, and ERIC databases. Based on the inclusion criteria, 18 studies yielding 23 effect sizes and involving a total of 1,823 participants were included in the analysis. Results: = 1.309). This result indicates that AI applications significantly improve individuals' vocabulary learning outcomes. Moderator analyses revealed significant differences according to language type, learner educational level, control treatment, and intervention setting. In contrast, teacher involvement level and intervention duration did not produce statistically significant moderating effects. Discussion: Overall, the results suggest that AI-supported vocabulary instruction is highly effective in improving vocabulary learning performance. These findings indicate that AI applications can provide context-sensitive and personalized learning opportunities across different educational settings. The study contributes to the growing literature by offering a comprehensive synthesis of current evidence and highlighting the instructional potential of AI in vocabulary teaching.