ThemeViz: Understanding the Effect of Human-AI Collaboration in Theme Development with an LLM-enhanced Interactive Visual System
Year: 2025
Type: article
Abstract: This paper explores the potential role of AI, e.g., large language models (LLMs), in supporting theme development in thematic analysis. While prior applications of AI in qualitative data analysis have focused on supporting coding, we investigate whether LLMs can effectively contribute as collaborators in the more abstract and conceptual phases of qualitative analysis, specifically theme development. Despite growing interest in AI as a collaborator in theme development, there is limited empirical evidence on designing AI-assisted tools while supporting user autonomy and understanding researcher interaction with AI-assisted theme development. To address this gap, we designed ThemeViz, an interactive system that uses GPT-4 to generate and visualize multiple versions of themes based on user input while allowing researchers to maintain control through manual coding and theme development. Our study examines the effectiveness of this human-AI collaboration approach in iterative theme development and its implications for future designs.
DOI: 10.1145/3757675
Authors: Daye Kang, Zhuolun Han, Jiahe Tian, Muhan Zhang, Jeffrey M Rzeszotarski
Institution: Cornell University
Language: English
Subfield: General Social Sciences
Field: Social Sciences
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W4415250153