Sharma, Ansh2025-08-252025-08-252025-08-252025-08-18https://hdl.handle.net/10012/22253Reflexive 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.enMaster Thesis