Exploring the Role of Large Language Models in Translation Education: A Systematic Review

Abstract

Despite the surge of research interest in generative AI and the rapid public adoption of large language models (LLMs), their role in translation remains unclear. The reliability of these systems and their limitations as machine translation tools continue to be a central concern for translation teachers and students. Systematic reviews that specifically examine LLMs in translation are still scarce. This systematic review aims to address this gap by synthesizing and interpreting recent empirical studies on the use of LLMs in translation across three areas: (1) LLMs’ translation quality, (2) LLM-generated translation feedback, and (3) the integration of LLMs into translation education. Drawing on 55 empirical studies, the findings show that LLMs—particularly GPT—consistently outperform conventional neural MT systems. For general, non-specialized texts, their output often approaches human quality, though human translators maintain a clear advantage in culturally dense, technical, or literary content. Evidence further indicates that LLMs can provide helpful and timely feedback that identifies common linguistic issues, which in turn can assist both teachers and students; however, teacher feedback remains superior in depth, contextual sensitivity, and clarity. As contemporary translation workplaces increasingly rely on MT and AI-supported tools, training students to work with LLMs has become essential for aligning classroom practice with professional expectations. At the same time, educators must balance LLM-assisted tasks with hands-on human translation to ensure that students continue to develop essential linguistic and problem-solving skills.