DeTAILS: Deep Thematic Analysis with Iterative LLM Support

2025-10-20

AI Summary

Qualitative thematic analysis (TA) faces scalability challenges due to its heavy reliance on researcher subjectivity and iterative reflexivity. This paper proposes a human-AI collaborative framework for AI-assisted TA, grounded in Braun & Clarke’s six-phase method. It leverages large language models (LLMs) to automate initial coding and preliminary theme generation, while an interactive interface enables researchers to provide real-time feedback and iteratively refine clustering, theme naming, and thematic synthesis. The framework preserves researcher agency and methodological transparency while enhancing analytical efficiency and reflective depth. An empirical user evaluation (N=18) demonstrates high alignment between AI-generated outputs and expert revisions, a 42% reduction in workload, a 3.1× speedup in analysis time, and a mean subjective usefulness rating of 4.6/5.0—validating its feasibility and practical utility for qualitative research.

Technology Category

• Humans and AI: Human-AI Collaboration / Human-AI Teaming
• Knowledge Representation and Reasoning: Qualitative Reasoning
• Application Domains: Humanities & Computational Social Science

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

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.

Problem

• Scaling thematic analysis in qualitative research through LLM integration
• Reducing researcher workload while preserving analytical agency in coding
• Enhancing trust and transparency in AI-assisted qualitative data analysis

Innovation

• Integrates LLM assistance into thematic analysis workflow
• Supports interactive feedback loops for code refinement
• Preserves researcher agency through transparent AI collaboration