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
주엽 (홍익대학교) 나건 (홍익대학교)
저널정보
한국디자인리서치학회 한국디자인리서치학회 한국디자인리서치 제9권 제1호
발행연도
2024.3
수록면
9 - 22 (14page)
DOI
https://doi.org/10.46248/kidrs.2024.1.9

이용수

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

초록· 키워드

오류제보하기
As the design field continues to expand, the boundaries of design have become increasingly blurred. The design field appeals to a rising population of individuals who are not design professionals with a strong passion for design. These individuals are commonly known as non-designers. Non-designers could attempt to create designs based on universal design thinking models and methods. However, the efficiency and quality of design remain a challenge for them. With the rapid development of AI and the emergence of Generative Artificial Intelligence(GAI) tools, new opportunities have arisen for this group of people. The purpose of this study is to explore the supplementary role of Generative AI in interdisciplinary design projects, to enhance design quality and efficiency. The study employed a mixed-methods research approach, combining qualitative and quantitative methods. First, we conducted a persona to gather user requirements. Then, we created a user journey map to illustrate the user's pain points. Finally, we analyzed the existing popular GAI tools that can help with design. As a result, we proposed a product design strategic framework for design collaboration with GAI, and a GAI card toolkit that can be used for product design to assist non-designers in enhancing design efficiency and quality in interdisciplinary design projects. To validate the research results, we conducted user testing. The findings of this study will assist non-designers to better accomplish their designs more effectively and encourage more designers to use GAI as an important tool for creative work.

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