Advanced EFL Learners’ Post-Editing of AI-generated Translations: Decisions Beyond Error Correction

초록

With rapid advances in machine translation (MT), post-editing is now a common practice in the translation industry as well as language learning. As MT continues to improve in fluency and accuracy, the challenge of post-editing has shifted from identifying and correcting errors to making nuanced interpretive and pragmatic choices. This exploratory study examines how 12 advanced Korean EFL learners revise ChatGPT-generated English-to-Korean translations during light post-editing (LPE) and full post-editing (FPE), with particular attention to the types of decisions they make throughout the post-editing process. For each learner, the number of words changed in each task was counted, and the revisions were analyzed together with the learners’ written reflections. The learners made minimal changes in LPE but in FPE reworked the output into natural, audience-appropriate Korean through recurring types of revision in register, tone, person reference, sentence structure, word choice, and idiomatic expressions. Students’ written reflections suggested that they view MT as a resource whose output should be evaluated against one’s own linguistic and cultural judgment. Pedagogical implications for MT post-editing instruction in language education are discussed.

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