A Bibliometric Analysis of AI-Mediated Communication in Second and Foreign Language Learning - Ervinda Dwi Meidyana (Lingua Franca, 2026)
Artificial Intelligence-Mediated Communication (AIMC) has become an increasingly influential area in second and foreign language learning, enabling new forms of interaction, feedback, and language support through AI technologies. Despite the rapid growth of this field, a comprehensive understanding of its research development, thematic structure, and emerging directions remains limited. This study maps the intellectual structure, thematic evolution, and research gaps in AIMC studies published between 2020 and 2025. Using bibliometric analysis and visualization techniques in VOSviewer, data were collected from the Scopus database and analyzed through keyword co-occurrence, thematic clustering, temporal overlay mapping, density visualization, and bibliographic coupling. A total of 521 publications were included in the analysis. The findings indicate a substantial increase in scholarly attention to AIMC in language education. Four dominant thematic areas emerged: generative AI and conversational systems; AI-assisted writing and automated evaluation; translation technologies; and socio-educational issues related to language learning. The results show particularly strong connections between AI technologies and writing-related applications, suggesting that writing remains the most extensively explored area in AIMC research. Temporal analysis also demonstrates a shift from earlier attention to machine translation toward generative AI and human–AI collaborative learning practices. However, several areas remain underexplored, particularly AI applications for speaking development, teacher professional development, assessment redesign, and non-English language contexts. These findings provide directions for future research and support more balanced, theoretically informed, and pedagogically meaningful integration of AIMC in language education.