Affording Process Auditability with QualAnalyzer: An Atomistic LLM Analysis Tool for Qualitative Research
arXiv:2604.03820v1 Announce Type: new Abstract: Large language models are increasingly used for qualitative data analysis, but many workflows obscure how analytic conclusions are produced. We present QualAnalyzer, an open-source Chrome extension for Google Workspace that supports atomistic LLM analysis by processing each data segment independently and preserving the prompt, input, and output for every unit. Through two case studies -- holistic essay scoring and deductive thematic coding of interview transcripts -- we show that this approach creates a legible audit trail and helps researchers investigate systematic differences between LLM and human judgments. We argue that process auditability is essential for making LLM-assisted qualitative research more transparent and methodologically ro
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Abstract:Large language models are increasingly used for qualitative data analysis, but many workflows obscure how analytic conclusions are produced. We present QualAnalyzer, an open-source Chrome extension for Google Workspace that supports atomistic LLM analysis by processing each data segment independently and preserving the prompt, input, and output for every unit. Through two case studies -- holistic essay scoring and deductive thematic coding of interview transcripts -- we show that this approach creates a legible audit trail and helps researchers investigate systematic differences between LLM and human judgments. We argue that process auditability is essential for making LLM-assisted qualitative research more transparent and methodologically robust.
Comments: 9 pages, 3 figures, BEA2026 Conference Submission
Subjects:
Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
Cite as: arXiv:2604.03820 [cs.AI]
(or arXiv:2604.03820v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2604.03820
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Max Lu [view email] [v1] Sat, 4 Apr 2026 18:07:05 UTC (1,070 KB)
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