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
질문

논문 기본 정보

자료유형
학술저널
저자정보
신종규 (금오공과대학교) 조인권 (금오공과대학교) 임완수 (금오공과대학교) 김상호 (금오공과대학교)
저널정보
대한인간공학회 대한인간공학회지 대한인간공학회지 제39권 제1호
발행연도
2020.2
수록면
73 - 86 (14page)
DOI
10.5143/JESK.2020.39.1.73

이용수

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

초록· 키워드

오류제보하기
Objective: The aim of this study is to identify a few critical design parameters for enhancing user"s satisfaction while interacting with the AI-infused intelligent systems through voice user interface (VUI).

Background: The interaction between the user and the AI-infused system is called as Human-AI Interaction (HAII) and supposed to have different features with respect to the human-computer interaction (HCI). It is therefore necessary to establish new criteria for designing and evaluating HAII in the point of user"s satisfaction.

Method: This study identified 31 user requirements regarding with HAII from previous studies and organized them into 9 secondary and 3 tertiary level user requirement categories. It was investigated and selected 9 design parameters of VUI that might make differences in user"s satisfaction. The priority of each design parameter was calculated using quality function deployment (QFD) technique.

Results: The amount of information, error control, and length of answer were found as the top three critical design parameters among others. They accounted for 51% of the total criticality score. It implies the reliability of information that the AI-infused systems provide during interaction is the most important factor for enhancing user"s satisfaction.

Conclusion: This study suggested theoretically nine critical interaction parameters and their priority in designing VUI embedded in AI-infused systems.

Application: The result of the study can be used to derive various experimental research models and hypothesis in HAII.

목차

1. Introduction
2. Method
3. Results
4. Discussion
5. Conclusion
References

참고문헌 (25)

참고문헌 신청

함께 읽어보면 좋을 논문

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

이 논문의 저자 정보

이 논문과 함께 이용한 논문

최근 본 자료

전체보기

댓글(0)

0

UCI(KEPA) : I410-ECN-0101-2020-530-000409566