Co-Refine: AI-Powered Tool Supporting Qualitative Analysis

• URL: http://arxiv.org/abs/2604.19309v1
• Date: Tue, 21 Apr 2026 10:16:13 GMT
• Title: Co-Refine: AI-Powered Tool Supporting Qualitative Analysis
• Abstract summary: We present Co-Refine, an AI-augmented qualitative coding platform that delivers continuous, grounded feedback on coding consistency.<n>The system employs a three-stage audit pipeline: Stage 1 computes deterministic embedding-based metrics for mathematical consistency; Stage 2 grounds LLM verdicts within $pm0.15$ of the deterministic scores; Stage 3 produces code definitions from previous patterns to create a deepening feedback loop.
• Score: 6.199710730080277
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 $\pm0.15$ 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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