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

Summary

Researchers propose the Smart Coder Framework, a three-phase approach using instruction, prompt strategy, and reliability checks to structure LLM-assisted qualitative coding, validated on 72 middle-school mathematics teacher interviews to improve agreement while preserving human authority.

SourcearXiv AIAuthor: 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
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[Submitted on 23 Oct 2026]

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

View a PDF of the paper titled Scaling Qualitative Analysis with Large Language Models: Development and Validation of the Smart Coder Framework for Human-LLM Collaborative Coding, by Cheng Tang and 6 other authors

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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 three-phase approach structuring human-LLM collaborative coding through Instruction, Prompt Strategy, and Reliability Check. We evaluated the framework using 72 middle-school mathematics teacher interview transcripts and a human-consensus benchmark. A factorial design compared instructed versus uninstructed prompts, few-shot versus few-shot plus chain-of-thought prompting, and single-pass versus self-consistency coding. Twelve transcripts were used for prompt development and calibration, and 60 for the formal evaluation. In the results, instruction consistently improved agreement. Chain-of-thought further improved agreement under adequate instruction, and self-consistency produced smaller, theme-dependent gains but helped identify unstable decisions for human review. Performance also varied by transcript characteristics. Overall, the framework systematically structures and monitors LLM-assisted qualitative coding while preserving human authority over coding decisions.

Comments: 29 pages, including supplemental materials

Subjects:

Artificial Intelligence (cs.AI)

Cite as: arXiv:2610.33205 [cs.AI]

(or arXiv:2610.33205v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2610.33205

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Cheng Tang [view email] [v1] Fri, 23 Oct 2026 18:52:09 UTC (830 KB)

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Key points

  • The Smart Coder Framework structures human-LLM collaborative coding into instruction, prompt strategy, and reliability check phases
  • Evaluation used 72 middle-school mathematics teacher interview transcripts and a human-consensus benchmark with a factorial design
  • Explicit instruction consistently improved coding agreement, and chain-of-thought further improved agreement under adequate instruction
  • Self-consistency produced smaller, theme-dependent gains and helped flag unstable decisions for human review

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