AI-Assisted Translation Teaching and Learning in Higher Education: A Systematic Review of Empirical Studies

Abstract

Artificial Intelligence increasingly shapes translation teaching and learning in tertiary education and has attracted substantial scholarly attention in recent years. Nevertheless, existing reviews largely investigate AI from a broad language education perspective or prioritise technological performance, leaving translation education insufficiently examined. Against this backdrop, this review analyses the benefits, challenges, and strategies associated with the use of AI tools in translation teaching and learning at the tertiary level. Through critical synthesis of related literature, the review shows scholars have largely overlooked how AI tools reshape pedagogical practices and influence learner experiences in translation education. In response, the study aims to propose a structured roadmap to advance the research and practice of translation teaching and learning. Methodologically, the study follows the Preferred Reporting Items for Systematic Reviews and Meta-Analysis protocols. Guided by these standards, 32 peer-reviewed journal articles were retrieved from the Web of Science, Scopus, and the Education Resources Information Centre databases, ensuring transparency and methodological rigor. The findings indicate that AI adoption enhances translation quality, strengthens learner engagement, and supports classroom innovation, while also raising challenges related to ethics, AI literacy, and teacher training. Based on these benefits and challenges, it is suggested that AI should be used as a tool for empowerment instead of a shortcut. Notably, the review highlights the need for focused research on Generative AI, alongside explicit ethical guidelines and concrete pedagogical modules to guide effective integration. Collectively, these recommendations provide practical guidance for educators, learners, and policymakers in navigating an AI-enhanced academic and professional environment.

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