# DeTAILS: Deep Thematic Analysis with Iterative LLM Support

arXiv: https://arxiv.org/abs/2510.17575 · HTML: https://mlsanity.com/paper/2510.17575/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Oct 2025.

## Built on by (most cited first, 5 of 5)

- [Bridging Human Interpretation and Machine Representation: A Landscape of Qualitative Data Analysis in the LLM Era](https://mlsanity.com/paper/2601.11739/) (2026)
- [Automated Thematic Analysis for Clinical Qualitative Data: Iterative Codebook Refinement with Full Provenance](https://mlsanity.com/paper/2603.08989/) (2026)
- [Can LLMs Understand the Impact of Trauma? Costs and Benefits of LLMs Coding the Interviews of Firearm Violence Survivors](https://mlsanity.com/paper/2604.16132/) (2026)
- [Co-Refine: AI-Powered Tool Supporting Qualitative Analysis](https://mlsanity.com/paper/2604.19309/) (2026)
- [Computational KJ-Ho: An Analyst-Bias-Free Insight Extraction Framework from Large-Scale Qualitative Data Using Domain-Specialized LLMs](https://mlsanity.com/paper/2608.16467/) (2026)

## Abstract (arXiv)

Thematic analysis is widely used in qualitative research but can be difficult to scale because of its iterative, interpretive demands. We introduce DeTAILS, a toolkit that integrates large language model (LLM) assistance into a workflow inspired by Braun and Clarke's thematic analysis framework. DeTAILS supports researchers in generating and refining codes, reviewing clusters, and synthesizing themes through interactive feedback loops designed to preserve analytic agency. We evaluated the system with 18 qualitative researchers analyzing Reddit data. Quantitative results showed strong alignment between LLM-supported outputs and participants' refinements, alongside reduced workload and high perceived usefulness. Qualitatively, participants reported that DeTAILS accelerated analysis, prompted reflexive engagement with AI outputs, and fostered trust through transparency and control. We contribute: (1) an interactive human-LLM workflow for large-scale qualitative analysis, (2) empirical evidence of its feasibility and researcher experience, and (3) design implications for trustworthy AI-assisted qualitative research.