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New framework structures LLM-assisted qualitative coding with human oversight

By PulseAugur Editorial · [1 sources] ·

Researchers have developed the Smart Coder Framework, a structured three-phase approach to enhance human-LLM collaboration in qualitative coding. This framework, comprising Instruction, Prompt Strategy, and Reliability Check phases, aims to improve the reliability and human oversight of LLM-assisted qualitative analysis. Evaluations using teacher interview transcripts demonstrated that explicit instructions consistently improved agreement, while Chain-of-Thought prompting further enhanced this agreement when combined with adequate instruction. The framework systematically guides and monitors LLM coding processes, ensuring human decisions remain authoritative. AI

IMPACT This framework could standardize and improve the reliability of qualitative data analysis using LLMs in research settings.

RANK_REASON The cluster contains an academic paper detailing a new framework for LLM-assisted qualitative analysis. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New framework structures LLM-assisted qualitative coding with human oversight

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The cluster contains an academic paper detailing a new framework for LLM-assisted qualitative analysis. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Cheng Tang, Kun Wang, Yaxuan Yang, Shiyu Wang, Allan Cohen, Rachael Eriksen Brown, Chandra Hawley Orrill ·

    Scaling Qualitative Analysis with Large Language Models: Development and Validation of the Smart Coder Framework for Human-LLM Collaborative Coding

    arXiv:2610.33205v1 Announce Type: new Abstract: Current uses of large language models (LLMs) for qualitative coding often rely on ad hoc prompting with limited guidance for reliability and human oversight. To address these limitations, we propose the Smart Coder Framework, a thre…

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