Co-Refine: AI-Powered Tool Supporting Qualitative Analysis
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Abstract (arXiv)
Qualitative coding relies on a researcher's application of codes to textual data. As coding proceeds across large datasets, interpretations of codes often shift (temporal drift), reducing the credibility of the analysis. Existing Computer-Assisted Qualitative Data Analysis (CAQDAS) tools provide support for data management but offer no workflow for real-time detection of these drifts. We present Co-Refine, an AI-augmented qualitative coding platform that delivers continuous, grounded feedback on coding consistency without disrupting the researcher's workflow. The system employs a three-stage audit pipeline: Stage 1 computes deterministic embedding-based metrics for mathematical consistency; Stage 2 grounds LLM verdicts within ±0.15 of the deterministic scores; and Stage 3 produces code definitions from previous patterns to create a deepening feedback loop. Co-Refine demonstrates that deterministic scoring can effectively constrain LLM outputs to produce reliable, real-time audit signals for qualitative analysis.
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References · 6
- 2024Concept Induction: Analyzing Unstructured Text with High-Level Concepts Using LLooMcited by 78
- 2024'Simulacrum of Stories': Examining Large Language Models as Qualitative Research Participantscited by 28
- 2023CoAIcoder: Examining the Effectiveness of AI-assisted Human-to-Human Collaboration in Qualitative Analysiscited by 25
- 2025LOGOS: LLM-driven End-to-End Grounded Theory Development and Schema Induction for Qualitative Researchcited by 6
- 2025DeTAILS: Deep Thematic Analysis with Iterative LLM Supportcited by 5
- 2024Mitigating Trauma in Qualitative Research Infrastructure: Roles for Machine Assistance and Trauma-Informed Designcited by 1
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