Artificial Intelligence in Language Teaching in the Context of Higher Vocational Education: A Bibliometric Mapping of Research Trends (2000–2024)

The integration of artificial intelligence (AI) into language education has gained remarkable momentum over the past two decades. As researchers and educators explore how AI can transform teaching and learning practices, a growing body of literature has emerged to investigate its pedagogical, technological, and ethical implications. This study aims to provide a comprehensive bibliometric analysis of scholarly research on AI in language teaching from 2000 to 2024 in context of higher vocational education. Based on 475 peer-reviewed articles retrieved from the Scopus database, this research employs VOSviewer and Excel to map the intellectual landscape of the field. Key analyses include keyword co-occurrence, author collaboration networks, and temporal publication trends. The findings indicate a significant rise in research activity beginning in 2016, with dominant themes such as intelligent tutoring systems, natural language processing, AI-assisted writing tools, and ethical concerns in educational technology. This study not only identifies conceptual clusters and high-impact publications but also highlights gaps in empirical and longitudinal research. It offers valuable insights for scholars seeking to explore emerging directions in AI-assisted language education. For practitioners, the results emphasize the importance of pedagogical alignment, ethical AI use, and targeted teacher training. The paper concludes by suggesting future avenues for research that integrate interdisciplinary approaches and address the evolving challenges of AI implementation in language training for higher vocational education.