The Collaborative Qualitative Analysis (CQA) process can be time-consuming and resource-intensive, requiring multiple discussions among team members to refine codes and ideas before reaching a consensus. To address these challenges, we introduce CollabCoder, a system leveraging Large Language Models (LLMs) to support three CQA stages: independent open coding, iterative discussions, and the development of a final codebook. In the independent open coding phase, CollabCoder provides AI-generated code suggestions on demand, and allows users to record coding decision-making information (e.g. keywords and certainty) as support for the process. During the discussion phase, CollabCoder helps to build mutual understanding and productive discussion by sharing coding decision-making information with the team. It also helps to quickly identify agreements and disagreements through quantitative metrics, in order to build a final consensus. During the code grouping phase, CollabCoder employs a top-down approach for primary code group recommendations, reducing the cognitive burden of generating the final codebook. An evaluation involving 16 users confirmed the usability and effectiveness of CollabCoder and offered empirical insights into the LLMs' roles in CQA.

翻译：协作式定性分析（CQA）过程通常耗时且资源密集，需要团队成员多次讨论以完善编码和想法，直至达成共识。为应对这些挑战，我们引入了CollabCoder——一个利用大语言模型（LLMs）支持CQA三个阶段（独立开放式编码、迭代讨论及最终编码手册开发）的系统。在独立开放式编码阶段，CollabCoder可按需提供AI生成的编码建议，并允许用户记录编码决策信息（如关键词和置信度）以支持该过程。在讨论阶段，CollabCoder通过向团队共享编码决策信息，帮助建立相互理解和富有成效的讨论。同时，它通过量化指标快速识别共识与分歧，从而构建最终共识。在代码分组阶段，CollabCoder采用自上而下的方法进行主要代码组推荐，减轻生成最终编码手册的认知负担。一项涉及16名用户的评估验证了CollabCoder的可用性和有效性，并为LLMs在CQA中的作用提供了实证见解。