While the domain of individual-level AI-assisted analysis has been
extensively explored in previous studies, the field of AI-assisted
collaborative qualitative analysis remains relatively unexplored. After
identifying CQA practices and design opportunities through formative
interviews, we introduce our collaborative qualitative coding tool, CoAIcoder,
and designed the four different collaboration methods. We subsequently
implemented a between-subject design involving 32 pairs of users who have
undergone training in CQA across three commonly utilized phases under four
methods. Our results suggest that CoAIcoder, which employs AI and a Shared
Model, could potentially improve the efficiency of the coding process in CQA by
fostering a quicker shared understanding and promoting early-stage discussions.
However, this may come with the potential downside of reduced code diversity.
We also underscored the existence of a trade-off between the level of
independence and the coding outcome when humans collaborate during the early
coding stages. Lastly, we identify design implications that could inspire and
inform the future design of CQA systems