🤖 AI Summary
This study examines the tension between efficiency gains and researcher autonomy arising from AI-assisted analysis in qualitative research. Through in-depth interviews with 16 qualitative researchers, it comparatively analyzes acceptance and underlying mechanisms across three coding paradigms: fully manual, human-initiated AI-assisted, and AI-initiated. The study innovatively conceptualizes AI explicitly as a “supporter”—neither collaborator nor supervisor—and identifies three core determinants of adoption: efficiency enhancement, attribution of interpretive ownership, and algorithmic trust. Findings indicate broad acceptance of AI for accelerating coding and thematic analysis, yet strong consensus on human primacy in meaning-making and interpretive authority. Enhancing procedural transparency, researcher control, and structured human–AI collaboration significantly strengthens trust and mitigates bias risks. The work provides theoretical grounding and actionable guidelines for developing human-centered, accountable AI-augmented qualitative research workflows.
📝 Abstract
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.