Bibliometric Review on Artificial Intelligence-Based Tools for Language Learning

Abstract

This bibliometric analysis uses data from the Scopus database to analyze the trends, impact, and evolution of research on AI-based language learning tools between 2018 and 2025. In this expanding sector, the study highlights important authors, significant organizations, and well-known publication sources. The findings show a notable rise in scientific output with a growth rate of 67.79%, especially in 2024 (266 publications), 2023 (195), and 2022 (115), which reflects the growing significance of AI technology in language learning. The information demonstrates how technologically advanced countries like China (276), Italy (168), and the United States (647 documents) dominate research production. Despite an increase in publications, the authorship pattern shows that only 0.6% of contributors have produced three or more papers, while 94.8% have only published one. This suggests that the most reliable contributors to the area are a select few researchers, such as DENNY P, LIU X, and WANG L. Leading journals for sharing research on AI language learning include IEEE ACCESS, APPLIED SCIENCES (SWITZERLAND), and PROCEDIA COMPUTER SCIENCE. The study emphasizes how deep learning, adaptive learning models, and natural language processing are becoming increasingly important topics. To guarantee that AI-based language learning tools successfully handle a variety of educational situations, future research should prioritize long-term studies, cross-disciplinary collaboration, and global inclusivity.