DeTAILS: Deep Thematic Analysis with Iterative LLM Support

Abstract

Reflexive thematic analysis (TA) yields rich insights but is challenging to scale to large datasets due to the intensive, iterative interpretation it requires. We present DeTAILS: Deep Thematic Analysis with Iterative LLM Support, a researcher-centered toolkit that integrates large language model (LLM) assistance into each phase of Braun \& Clarke’s six-phase reflexive TA process through iterative human-in-the-loop workflows. DeTAILS introduces key features such as “memory snapshots” to incorporate the analyst’s insights, “redo-with-feedback” loops for iterative refinement of LLM suggestions, and editable LLM-generated codes and themes, enabling analysts to accelerate coding and theme development while preserving researcher control and interpretive depth. In a user study with 18 qualitative researchers (novice to expert) analyzing a large, heterogeneous dataset, DeTAILS demonstrated high usability. The study also showed that chaining LLM assistance across analytic phases enabled scalable yet robust qualitative analysis. This work advances Human-LLM collaboration in qualitative research by demonstrating how LLMs can augment reflexive thematic analysis without compromising researcher agency or trust.