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This study systematically examines the role of Artificial Intelligence (AI) in translation and interpreting in educational contexts. A systematic literature review (SLR) was conducted following the PRISMA 2020 framework, analysing 38 studies published between 2020 and March 2026. The review adopts a hybrid methodology that combines automated relevance scoring—implemented through a Python 3.13-based rule-driven model—with manual validation to support screening efficiency and methodological transparency. The findings reveal the predominance of neural machine translation (NMT) and large language models (LLMs), which several of the reviewed studies associate with potential improvements in translation quality, efficiency, and accessibility, although these effects are not consistent across all contexts. These technologies support several educational applications, including language learning, translator training, automated feedback, and multilingual educational content access. However, persistent challenges remain, including limitations in handling cultural and contextual nuances, reduced performance in specialised domains, and persistent disparities in low-resource languages, largely driven by data scarcity and limited linguistic representation. Additional concerns include student assessment, technological dependency, and the evolving roles of educators and translation professionals. This study offers a structured synthesis of trends, applications, and challenges, highlighting the need for hybrid and inclusive AI approaches to address linguistic diversity, particularly in low-resource contexts.
Candé et al. (Tue,) studied this question.