메뉴 건너뛰기
.. 내서재 .. 알림
소속 기관/학교 인증
인증하면 논문, 학술자료 등을  무료로 열람할 수 있어요.
한국대학교, 누리자동차, 시립도서관 등 나의 기관을 확인해보세요
(국내 대학 90% 이상 구독 중)
로그인 회원가입 고객센터 ENG
주제분류

추천
검색

논문 기본 정보

자료유형
학술저널
저자정보
Sang-Jun Kim (Korea Research Institute of Bioscience and Biotechnology)
저널정보
한국과학학술지편집인협의회 Science Editing Science Editing Vol.11 No.2
발행연도
2024.8
수록면
96 - 106 (11page)
DOI
https://doi.org/10.6087/kcse.343

이용수

표지
📌
연구주제
📖
연구배경
🔬
연구방법
🏆
연구결과
AI에게 요청하기
추천
검색

초록· 키워드

오류제보하기
While generative artificial intelligence (AI) technology has become increasingly competitive since OpenAI introduced ChatGPT, its widespread use poses significant ethical challenges in research. Excessive reliance on tools like ChatGPT may intensify ethical concerns in scholarly articles. Therefore, this article aims to provide a comprehensive narrative review of the ethical issues associated with using AI in academic writing and to inform researchers of current trends. Our methodology involved a detailed examination of literature on ChatGPT and related research trends. We conducted searches in major databases to identify additional relevant articles and cited literature, from which we collected and analyzed papers. We identified major issues from the literature, categorized into problems faced by authors using nonacademic AI platforms in writing and challenges related to the detection and acceptance of AI-generated content by reviewers and editors. We explored eight specific ethical problems highlighted by authors and reviewers and conducted a thorough review of five key topics in research ethics. Given that nonacademic AI platforms like ChatGPT often do not disclose their training data sources, there is a substantial risk of unattributed content and plagiarism. Therefore, researchers must verify the accuracy and authenticity of AI-generated content before incorporating it into their article, ensuring adherence to principles of research integrity and ethics, including avoidance of fabrication, falsification, and plagiarism.

목차

등록된 정보가 없습니다.

참고문헌 (0)

참고문헌 신청

함께 읽어보면 좋을 논문

논문 유사도에 따라 DBpia 가 추천하는 논문입니다. 함께 보면 좋을 연관 논문을 확인해보세요!

최근 본 자료

전체보기

댓글(0)

0