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
Seung Jin Chung (Yonsei University) Hyunju Lee (Yonsei University)
저널정보
한국HCI학회 한국HCI학회 논문지 한국HCI학회 논문지 2020 Vol.15 No.2
발행연도
2020.6
수록면
39 - 45 (7page)
DOI
10.17210/jhsk.2020.06.15.2.39

이용수

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

초록· 키워드

오류제보하기
Today the digital healthcare market and interests in mental health chatbots are growing. People get easily accessed to chatbot interactions, and chatbots can be used to support people’s emotional stability and psychological well-being. Consequently, many mental healthcare chatbots have been designed to reduce the likelihood of the occurrence of mental health issues by improving self-metacognition through early interventions. However, only few studies have discussed specific factors, which mental healthcare chatbot design may affect user experience. Besides, although visual presentations throughout a chatbot system influence users’ positive/negative experiences, most chatbot studies so far have focused on identity design rather than graphical interfaces and non-verbal visual communication tools. While it is important to examine specific visual design elements, it is also important to examine overall visual design requirements. Therefore, this study explored the user experience of mental health chatbots in terms of identity design, chatbot interface design, and visual communication tools. In this study, participants’ data were collected by pre/post preference evaluations, the system usability scale questionnaire, and semi-structured interview related to selected chatbot systems (i.e. Replika, Youper, Sayana, Woebot). The collected data were qualitatively analysed, and consequently, considerations were suggested for designing mental healthcare chatbots.

목차

Abstract
1. Introduction
2. Literature Reviews
3. Methods
4. Results
5. Discussion
6. Conclusion
Reference

참고문헌 (28)

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-0101-2020-004-000877845