Designing an Auditable LLM-Supported Workflow for Qualitative Thematic Analysis
Large Language Models (LLMs) offer new possibilities for scaling qualitative analysis, but existing applications often provide limited methodological transparency regarding how qualitative methods are translated into computational procedures. This paper presents an auditable and privacy-preserving computational operationalization of inductive and latent Thematic Analysis (TA). This paper first derives five design principles from the methodological requirements of TA and the conditions introduced by LLM-based inference: preserving interpretative context, maintaining traceable relationships between empirical material and analytical outputs, representing analytical constructs and reasoning e...
| Authors | Nadia Jul Jeldtoft, Tariq Yousef |
|---|---|
| Primary category | cs.AI |
| Published | 31 August 2026 |
| Last updated at source | 31 August 2026 |
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