Generative artificial intelligence (GenAI) offers promising potential for advancing human-AI collaboration in qualitative research. However, existing works focused on conventional machine-learning and pattern-based AI systems, and little is known about how researchers interact with GenAI in qualitative research. This work delves into researchers' perceptions of their collaboration with GenAI, specifically ChatGPT. Through a user study involving ten qualitative researchers, we found ChatGPT to be a valuable collaborator for thematic analysis, enhancing coding efficiency, aiding initial data exploration, offering granular quantitative insights, and assisting comprehension for non-native speakers and non-experts. Yet, concerns about its trustworthiness and accuracy, reliability and consistency, limited contextual understanding, and broader acceptance within the research community persist. We contribute five actionable design recommendations to foster effective human-AI collaboration. These include incorporating transparent explanatory mechanisms, enhancing interface and integration capabilities, prioritising contextual understanding and customisation, embedding human-AI feedback loops and iterative functionality, and strengthening trust through validation mechanisms.

翻译：生成式人工智能(GenAI)为推进定性研究中的"人机协作"展现出广阔前景。然而,现有研究主要集中于传统机器学习和基于模式的AI系统,学界对研究人员在定性研究中如何与生成式AI交互知之甚少。本研究深入探讨了研究人员对与GenAI(特别是ChatGPT)协作的认知。通过一项涉及十名定性研究人员的用户实验,我们发现ChatGPT是主题分析中的宝贵协作者,能提升编码效率、辅助数据初步探索、提供细粒度的定量见解,并帮助非母语研究者和非专业人士理解内容。然而,对其可信度、准确性、可靠性与一致性、有限的情境理解能力以及研究社区内的广泛接受度仍存隐忧。我们提出了五项具有可操作性的设计建议以促进有效的人机协作:包含引入透明的解释机制、增强界面与集成能力、优先考虑情境理解与定制化、嵌入人机反馈循环与迭代功能,以及通过验证机制强化信任。