A Comparative Analysis of Research Hotspots, Development Trends Trajectories, and Future Direction in AI-Enabled Language Education: A Bibliometric and Visualization Analysis

Drawing on journal articles indexed in CNKI and the Web of Science Core Collection, this study conducts a systematic review and comparative analysis of Chinese and international research on AI-empowered foreign language and translation teaching. Specifically, 104 Chinese literature pieces published between 2020 and 2025, and 200 English literature pieces published between 2006 and 2025 were included in the survey. By adopting bibliometric methods and knowledge mapping with CiteSpace, the study examines publication trends, institutional and author collaboration networks, keyword co-occurrence, and thematic clusters to reveal the knowledge structure and developmental trajectories of the field across different academic contexts. The findings show that Chinese research has grown rapidly since 2020, with major focuses on the application of generative artificial intelligence, curriculum reform, and the integration of value-oriented education. In contrast, English-language research has developed progressively over nearly two decades and has formed more internationalized and interconnected collaboration patterns. Beyond classroom practices, it places greater emphasis on teacher development, ethical governance, and methodological innovation, reflecting stronger theoretical depth and research rigor. Overall, the two research traditions present complementary orientations, namely an application-driven approach and a structurally reflective approach. This study contributes to a clearer understanding of the global landscape of AI integration in language and translation education and provides empirical evidence for future cross-context collaboration and methodological integration.