  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.
