Human-AI Collaborative Inductive Thematic Analysis: AI Guided Analysis and Human Interpretive Authority
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866914260759609344 |
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| author | Nyaaba, Matthew SungEun, Min Apam, Mary Abiswin Acheampong, Kwame Owoahene Dwamena, Emmanuel Zhai, Xiaoming |
| author_facet | Nyaaba, Matthew SungEun, Min Apam, Mary Abiswin Acheampong, Kwame Owoahene Dwamena, Emmanuel Zhai, Xiaoming |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2601_11850 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Human-AI Collaborative Inductive Thematic Analysis: AI Guided Analysis and Human Interpretive Authority Nyaaba, Matthew SungEun, Min Apam, Mary Abiswin Acheampong, Kwame Owoahene Dwamena, Emmanuel Zhai, Xiaoming Artificial Intelligence Computers and Society 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. |
| title | Human-AI Collaborative Inductive Thematic Analysis: AI Guided Analysis and Human Interpretive Authority |
| topic | Artificial Intelligence Computers and Society |
| url | https://arxiv.org/abs/2601.11850 |