Human-AI Collaborative Inductive Thematic Analysis: AI Guided Analysis and Human Interpretive Authority
🤖 AI Summary
This study investigates how generative artificial intelligence can be effectively integrated into qualitative research while preserving the researcher’s interpretive authority and analytical primacy. To this end, the authors propose HACITA, a human-AI collaborative framework, and develop a dedicated tool, ITA-GPT, which employs structured prompts to support familiarization with textual data, verbatim coding, descriptive coding using gerunds and nouns, and theme generation. The tool incorporates features for text tracing, coverage verification, and auditability to ensure transparency and traceability throughout the analytical process. Researchers retain continuous interpretive control through functionalities enabling modification, deletion, insertion, and annotation of AI-generated outputs. The study demonstrates the feasibility of organically blending automated assistance with human judgment in inductive thematic analysis, offering a novel paradigm for responsible human-AI collaboration in qualitative inquiry.
Application Category
📝 Abstract
The increasing use of generative artificial intelligence (GenAI) in qualitative research raises important questions about analytic practice and interpretive authority. This study examines how researchers interact with an Inductive Thematic Analysis GPT (ITA-GPT), a purpose-built AI tool designed to support inductive thematic analysis through structured, semi-automated prompts aligned with reflexive thematic analysis and verbatim coding principles. Guided by a Human-Artificial Intelligence Collaborative Inductive Thematic Analysis (HACITA) framework, the study focuses on analytic process rather than substantive findings. Three experienced qualitative researchers conducted ITA-GPT assisted analyses of interview transcripts from education research in the Ghanaian teacher education context. The tool supported familiarization, verbatim in vivo coding, gerund-based descriptive coding, and theme development, while enforcing trace to text integrity, coverage checks, and auditability. Data sources included interaction logs, AI-generated tables, researcher revisions, deletions, insertions, comments, and reflexive memos. Findings show that ITA-GPT functioned as a procedural scaffold that structured analytic workflow and enhanced transparency. However, interpretive authority remained with human researchers, who exercised judgment through recurrent analytic actions including modification, deletion, rejection, insertion, and commenting. The study demonstrates how inductive thematic analysis is enacted through responsible human AI collaboration.
Problem
Research questions and friction points this paper is trying to address.
generative artificial intelligence
inductive thematic analysis
interpretive authority
human-AI collaboration
qualitative research
Innovation
Methods, ideas, or system contributions that make the work stand out.
Human-AI collaboration
Inductive Thematic Analysis
Generative AI
Interpretive authority
Reflexive coding
🔎 Similar Papers
No similar papers found.
