Human-LLM Collaborative Inductive Coding for Conceptualizing K-12 Educator AI Use
Qualitative researchers increasingly encounter interaction corpora whose scale exceeds what manual coding alone can address, and large language models (LLMs) are frequently proposed as analytic assistants. The open questions are not whether LLMs can participate in qualitative analysis but to what extent, in what phases, and under what safeguards. This article provides a detailed procedural account of a multi-phase human-LLM collaborative pipeline that adapted open, axial, and selective coding to develop a hierarchical codebook from 45,000 messages exchanged between K-12 educators and a generative AI platform. Across three phases, LLMs generated candidate labels and structured annotations ...
| Authors | Alex Liu, Min Sun, Lief Esbenshade et al. |
|---|---|
| Primary category | cs.HC |
| Published | 30 July 2026 |
| Last updated at source | 30 July 2026 |
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