Unveiling the role of AI tools in language learning: a bibliometric study

Abstract

Artificial intelligence (AI) tools increasingly mediate language teaching, learning, feedback, and interaction. This study uses bibliometric analysis to map the development, intellectual structure, and emerging themes of research on AI tools in language learning. The dataset comprises 2,036 peer-reviewed journal articles indexed in the Web of Science Core Collection. Descriptive analysis, bibliographic coupling, and co-word analysis show a marked rise in publication output after 2020 and identify six contemporary research fronts: conversational AI and learner psychology; cognitive, neurophysiological, and computational foundations; learner adoption, perceptions, and motivation; AI-mediated communication, writing support, and pedagogical readiness; social robotics and embodied interaction; and conversational agents in immersive environments. Four co-word clusters indicate emerging attention to intelligent analytics and automated support, adoption and generative AI tools, learner psychology and pedagogically driven design, and child-centered developmental and computational pathways. In comparison with recent systematic and scoping reviews, the study corroborates the rapid growth of conversational and generative AI research while extending the evidence longitudinally and across a wider range of technologies, sources, and thematic relationships. Because bibliometric patterns do not test learner behavior, technology acceptance, or learning effectiveness, the study presents the identified relationships as a conceptual agenda for future empirical research. The study is limited to English-language journal articles indexed in the selected Web of Science databases.