Peer-debriefing agents refining AI-generated qualitative codes from theory-, data-, or applied-driven perspectives align closer with human codes than a single-LLM pass, with distinct recall-precision trade-offs per perspective.
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3 Pith papers cite this work, alongside 1 external citations. Polarity classification is still indexing.
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CentaurTA Studio reaches up to 92.12% accuracy in open coding and theme construction across three domains by using a two-stage human feedback loop, persistent prompt optimization, and rubric-based early stopping, outperforming baselines with substantial human-LLM agreement.
Proposes a formal framework based on Interdependence Theory to select Levels of Automation for qualitative analysis stages by assessing task risk and validation cost, shown in a case study with three design principles.
citing papers explorer
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Agent-as-Peer-Debriefer: A Multi-Agent Framework with Perspective-Based Refinement for Qualitative Analysis
Peer-debriefing agents refining AI-generated qualitative codes from theory-, data-, or applied-driven perspectives align closer with human codes than a single-LLM pass, with distinct recall-precision trade-offs per perspective.
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CentaurTA Studio: A Self-Improving Human-Agent Collaboration System for Thematic Analysis
CentaurTA Studio reaches up to 92.12% accuracy in open coding and theme construction across three domains by using a two-stage human feedback loop, persistent prompt optimization, and rubric-based early stopping, outperforming baselines with substantial human-LLM agreement.
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Structuring Human-AI Productive Interdependence by Strategic Level of Automation Selection for Qualitative Inquiry
Proposes a formal framework based on Interdependence Theory to select Levels of Automation for qualitative analysis stages by assessing task risk and validation cost, shown in a case study with three design principles.