arXiv AI By Alex Liu, Min Sun, Lief Esbenshade, Michael Xiao, Victor Tian, Zachary Zhang, Kevin He

Human-LLM Collaborative Inductive Coding for Conceptualizing K-12 Educator AI Use

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arXiv:2607. 28889v1 Announce Type: cross Abstract: 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.

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arXiv AI
Sep 12

How AI Coders Discuss, Disagree, and Reach Consensus: Challenges and Opportunities for LLM-Based Qualitative Coding

The paper investigates how large language models (LLMs) can perform multi-coder qualitative coding by independently coding, debating, and reconciling disagreements. It quantifies the effectiveness of this approach across diverse datasets, identifying key factors—such as codebook length, data similarity, and agent disagreement—that influence coding accuracy. The study finds that intense, unresolved debates improve accuracy but that LLMs still lack adaptive responsiveness to context, leading to design recommendations for automated coding systems.

By Jeongyeon Kim, John Mitchell
arXiv AI
Aug 3

Agreement Is Not Quality: Blind Expert Verification of Human and LLM Qualitative Coding When Human Consensus Is Not Ground Truth

arXiv:2607. 28890v1 Announce Type: cross Abstract: Evaluations of LLM-assisted qualitative coding almost universally measure model performance as agreement with human coders, a practice that presumes human coding is the standard to approximate.

By Alex Liu, Lief Esbenshade, Michael Xiao, Victor Tian, Zachary Zhang, Kevin He, Min Sun