Co-Refine combines deterministic embedding metrics with LLM feedback in a three-stage pipeline to detect temporal drift in qualitative coding without disrupting the workflow.
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Interviews with 16 qualitative researchers identify efficiency, ownership, and trust as key factors shaping preferences for AI as a supportive assistant rather than a full collaborator or supervisor in qualitative data analysis.
The study introduces a framework and reports differences in consistency, cohesiveness, and correctness of themes produced under synchronous versus asynchronous collaboration across three interactive NLP tools.
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Co-Refine: AI-Powered Tool Supporting Qualitative Analysis
Co-Refine combines deterministic embedding metrics with LLM feedback in a three-stage pipeline to detect temporal drift in qualitative coding without disrupting the workflow.
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Not a Collaborator or a Supervisor, but an Assistant: Striking the Balance Between Efficiency and Ownership in AI-incorporated Qualitative Data Analysis
Interviews with 16 qualitative researchers identify efficiency, ownership, and trust as key factors shaping preferences for AI as a supportive assistant rather than a full collaborator or supervisor in qualitative data analysis.
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Effects of Collaboration on the Performance of Interactive Theme Discovery Systems
The study introduces a framework and reports differences in consistency, cohesiveness, and correctness of themes produced under synchronous versus asynchronous collaboration across three interactive NLP tools.