Not a Collaborator or a Supervisor, but an Assistant: Striking the Balance Between Efficiency and Ownership in AI-incorporated Qualitative Data Analysis

Qualitative research offers deep insights into human experiences, but its processes, such as coding and thematic analysis, are timeintensive 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.

CCS Concepts: • Human-centered computing → Collaborative and social computing design and evaluation methods; Collaborative and social computing theory, concepts and paradigms.