Earticle

초록

영어

This study systematically analyzes Korean scholarship on generative AI in translation and interpreting, examining 77 research articles published between 2023 and 2025 in three major Korean journals. Drawing on Christensen et al.‘s (2017) tripartite classification, the study develops an extended coding framework to map methodological trends and identify research gaps in the field. The findings reveal sharp increase in AI-related publications, rising from 14 articles in 2023 to 37 in 2025. Translation and interpreting education (37.7%) and quality evaluation (27.3%) dominate the research agenda, followed by theoretical and critical discussions (13.0%). Empirical studies constitute the majority (77.9%), with textual analysis and survey methods being the most prevalent. Korean-to-English language pair remains the most studied (70.4% of language-specific studies), while explicit theoretical grounding appears only in 27.2% of the articles. These patterns suggest the Korean translation and interpreting research in the generative AI era is largely practice-driven and pedagogy-centered in the era of AI. The study identifies key gaps — including the underrepresentation of prompt engineering, process-oriented and multilingual research — and calls for a more theoretically grounded and methodologically diversified research agenda.

목차

Abstract
I. 들어가는 말
II. 이론적 배경
1. 번역 기술의 진화와 연구 패러다임의 변천
2. 기술 번역 메타 연구의 내용과 의의
III. 연구방법
1. 연구 절차
2. 번역기술 연구 항목의 매트릭스 구성 과정
3. 세부 분석 변수의 코딩 항목 구성
IV. 분석 결과
1. 연구의 양적 급성장과 주제 집중화
2. 실증 연구 편향과 이론 공백의 문제
3. 연구 대상과 방법의 편향성
4. 새로운 패러다임의 출현
V. 나가는 말
참고문헌
부록: 분석 대상 논문 목록(가나다순)

저자

• 김순미 [ Kim, Soon Mi | 숙명여자대학교 영문학부 조교수 ]

참고문헌