A Bibliometric Analysis of Metaverse-Integrated Learning in Language Acquisition

Metaverse-integrated learning (MIL) has garnered increasing attention in educational fields in recent years. While prior research has predominantly focused on scientific fields, only a few scholars have delved into its application in language acquisition. This study aimed to delineate the intellectual landscape of MIL in language acquisition, identifying key regions/countries, authors and journals, and to scrutinize the roles of self-efficacy, collaborative learning, and multi-modality within this context. Based on a bibliometric analysis, this study utilized both quantitative and qualitative methods to examine 248 articles from the Web of Science, spanning from 2010 to 2025, with the aid of VOSviewer and CitNetExplorer. The findings indicate that the primary research hubs are located in Taiwan, the United States and China. Among the authors, Lan Yuju is the most influential, while Computer Assisted Language Learning emerges as the most frequently cited journal in the field. With a significant upward trend in the academic domain, the findings further reveal that self-efficacy, collaborative learning, and multi-modality are not only crucial but also interdependent factors in MIL in language acquisition. These findings may provide implications for relevant educators and policymakers to enhance the MIL in language acquisition within the educational world.