  While AI-assisted individual qualitative analysis has been substantially
studied, AI-assisted collaborative qualitative analysis (CQA)-a process that
involves multiple researchers working together to interpret data-remains
relatively unexplored. After identifying CQA practices and design opportunities
through formative interviews, we designed and implemented CoAIcoder, a tool
leveraging AI to enhance human-to-human collaboration within CQA through four
distinct collaboration methods. With a between-subject design, we evaluated
CoAIcoder with 32 pairs of CQA-trained participants across common CQA phases
under each collaboration method. Our findings suggest that while using a shared
AI model as a mediator among coders could improve CQA efficiency and foster
agreement more quickly in the early coding stage, it might affect the final
code diversity. We also emphasize the need to consider the independence level
when using AI to assist human-to-human collaboration in various CQA scenarios.
Lastly, we suggest design implications for future AI-assisted CQA systems.
