Earticle

초록

영어

This study compares generative AI feedback and instructor feedback in Korean-English scientific and technical translation education. The analysis is based on seven student translations of a corporate technical brochure on lithium-ion battery cathode precursors and environmental catalysts. Generative AI feedback was produced using the same semi-structured prompt for all translations, and instructor feedback was provided on the same texts. The two types of feedback were compared in terms of meaning and terminology, information structure and readability, genre appropriateness, and target-language quality. The findings show that generative AI feedback was useful for identifying unnatural expressions, suggesting terminology and collocations, improving sentence structure, and offering more natural English alternatives. However, its feedback tended to focus on sentence-level revision and required further evaluation in relation to source-text meaning, technical concepts, and genre function. Instructor feedback, by contrast, more explicitly addressed the reasons behind translation problems and linked them to translation decision-making. The findings suggest that generative AI feedback can support students’ self-revision, but instructor feedback remains important for helping students evaluate and apply AI-generated suggestions critically.

목차

Abstract
I. 들어가는 말
II. 이론적 배경
1. 생성형 AI 시대의 번역 교육과 교수자의 역할 변화
2. 번역 교육에서 피드백의 역할 및 AI 피드백
3. 과학기술 번역에 대한 피드백의 분석 차원
III. 연구방법
1. 분석 자료
2. 분석 절
3. 분석 기준
IV. 분석 결과
1. 의미와 용어의 정확성
2. 정보 구조와 가독성
3. 장르 적절성
4. 목표언어 완성도
5. 종합 논의
V. 나가는 말
참고문헌
<부록> 번역 과제 원문

키워드

저자

• 최효은 [ Choi, Hyoeun | 이화여자대학교 ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  통역과 번역 [Interpretation and Translation]

• 간기

  연3회

• pISSN

  1229-6074

• 수록기간

  1999~2026

• 등재여부

  KCI 등재

• 십진분류

  KDC 717 DDC 400