Early conversational agents (CAs) focused on dyadic human-AI interaction between humans and the CAs, followed by the increasing popularity of polyadic human-AI interaction, in which CAs are designed to mediate human-human interactions. CAs for polyadic interactions are unique because they encompass hybrid social interactions, i.e., human-CA, human-to-human, and human-to-group behaviors. However, research on polyadic CAs is scattered across different fields, making it challenging to identify, compare, and accumulate existing knowledge. To promote the future design of CA systems, we conducted a literature review of ACM publications and identified a set of works that conducted UX (user experience) research. We qualitatively synthesized the effects of polyadic CAs into four aspects of human-human interactions, i.e., communication, engagement, connection, and relationship maintenance. Through a mixed-method analysis of the selected polyadic and dyadic CA studies, we developed a suite of evaluation measurements on the effects. Our findings show that designing with social boundaries, such as privacy, disclosure, and identification, is crucial for ethical polyadic CAs. Future research should also advance usability testing methods and trust-building guidelines for conversational AI.
翻译:早期对话媒介(CAs)侧重于人类与CAs之间的人类-个体互动,随后是多类人-个体互动越来越受欢迎,在这种互动中,CAs旨在调解人类-人类互动。多类人-个体互动是独一无二的,因为它们包括混合的社会互动,即人-个体、人与人之间和人与群体之间的行为。然而,关于多类人-个体互动的研究分散在不同领域,对识别、比较和积累现有知识提出了挑战。为促进CA系统的未来设计,我们对ACM出版物进行了文献审查,并确定了开展UX(用户经验)研究的一套工作。我们从质量上综合了多类人-人类互动的影响,将其纳入到人类-人类互动的四个方面,即交流、接触、联系和关系维护。通过对选定的多类人-个体-个体-个体-个体-个体-个体-个体研究进行混合方法分析,我们开发了一套关于影响的评价测量方法。我们的调查结果显示,设计社会界限,例如隐私、披露和识别能力研究。我们对道德-信任的测试方法也至关重要。