[2604.03820] Affording Process Auditability with QualAnalyzer: An Atomistic LLM Analysis Tool for Qualitative Research

Computer Science > Artificial Intelligence arXiv:2604.03820 (cs) [Submitted on 4 Apr 2026] Title:Affording Process Auditability with QualAnalyzer: An Atomistic LLM Analysis Tool for Qualitative Research Authors:Max Hao Lu, Ryan Ellegood, Rony Rodriguez-Ramirez, Sophia Blumert View a PDF of the paper titled Affording Process Auditability with QualAnalyzer: An Atomistic LLM Analysis Tool for Qualitative Research, by Max Hao Lu and 3 other authors View PDF HTML (experimental) 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: 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 Focus to learn more arXiv-issued DOI ...