Qualitative research offers deep insights into human experiences, but its processes, such as coding and thematic analysis, are time-intensive and laborious. Recent advancements in qualitative data analysis (QDA) tools have introduced AI capabilities, allowing researchers to handle large datasets and automate labor-intensive tasks. However, qualitative researchers have expressed concerns about AI's lack of contextual understanding and its potential to overshadow the collaborative and interpretive nature of their work. This study investigates researchers' preferences among three degrees of delegation of AI in QDA (human-only, human-initiated, and AI-initiated coding) and explores factors influencing these preferences. Through interviews with 16 qualitative researchers, we identified efficiency, ownership, and trust as essential factors in determining the desired degree of delegation. Our findings highlight researchers' openness to AI as a supportive tool while emphasizing the importance of human oversight and transparency in automation. Based on the results, we discuss three factors of trust in AI for QDA and potential ways to strengthen collaborative efforts in QDA and decrease bias during analysis.

翻译：定性研究能深入洞察人类经验，但其编码与主题分析等过程耗时费力。近年来定性数据分析（QDA）工具的进步引入了AI能力，使研究者能够处理大规模数据集并自动化劳动密集型任务。然而，定性研究者对AI缺乏语境理解、可能掩盖其工作的协作性与阐释性本质表示担忧。本研究探究研究者对QDA中三种AI委派程度（纯人工编码、人工启动编码与AI启动编码）的偏好，并探索影响这些偏好的因素。通过对16位定性研究者的访谈，我们识别出效率、自主权与信任是决定期望委派程度的关键因素。研究结果凸显研究者对AI作为辅助工具的开放性，同时强调人工监督与自动化透明度的重要性。基于此，我们讨论了QDA中AI信任的三要素，以及强化QDA协作效能、降低分析偏见的潜在路径。