Exploring the Development of AI-Mediated Competence for Sustainable Translation and Interpreting Education: A Bibliometric Review
TL;DR
The keywords evidence points to a shift from machine translation, post-editing, and translation technology training toward AI literacy, evaluative judgement, output verification, feedback practices, ethical responsibility, professional agency, and human-AI collaboration.
Abstract
Artificial intelligence has become increasingly central to translation and interpreting education, shaping classroom practice, feedback, assessment, and professional preparation. Yet the literature remains fragmented across work on machine translation, computer-assisted translation, post-editing, automatic speech recognition, generative AI, large language models, and AI-supported assessment. This bibliometric review examines how research in this area has developed from 2014 to May 2026, with particular attention to the movement from tool-oriented technology training toward AI-mediated competence. Bibliographic records were retrieved from Web of Science and Scopus and analysed using VOSviewer and CiteSpace. The analysis focuses on publication trends, collaboration patterns, keyword co-occurrence, thematic clusters, and keyword bursts. The results show limited output before 2018, steady growth between 2019 and 2021, and rapid expansion after 2022. The keyword evidence points to a shift from machine translation, post-editing, and translation technology training toward AI literacy, evaluative judgement, output verification, feedback practices, ethical responsibility, professional agency, and human-AI collaboration. Interpreting-related research is still less developed than translation-oriented research. The findings suggest that AI integration in translation and interpreting education is not simply a matter of adopting new tools, but part of a broader process of competence development.
