Exploring the affordances of large language models in translation learning: an exploratory case study

Large language models (LLMs) are widely considered as potential game-changers for English as a foreign language (EFL) teaching and learning, and in particular, there is a growing body of research indicating the transformative role they may play in translation learning. However, little is known about how students actually use and benefit from LLMs in their translation learningand what obstacles they may encounter during their interaction with such tools. To address these gaps, this exploratory case study aimed to examine the affordances of LLMs for translation learning and the constraining factors that may affect learning experiences and outcomes. Thirty Chinese university students volunteered to participate in a 4-week translation project in which they utilized LLMs for translation practice in an out-of-class setting. The analysis revealed the presence of three types of affordances: (1) preparation-related affordances, (2) process-related affordances, and (3) product-related affordances. LLMs significantly enhanced translation learning experience by assisting with preparation, facilitating the process, and refining outputs. The findings also highlighted that LLMs’ assistive capability may be hampered due to either the issues inherent in the technical design of LLMs or learners’ cognitive weakness and insufficient AI literacy. The study holds pedagogical implications concerning the instruction and task design for LLM-aided translation learning.