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.

翻译：生成式人工智能在质性研究中的日益广泛应用引发了关于分析实践与解释权威的重要问题。本研究探讨研究者如何与归纳式主题分析GPT进行交互——这是一种专门设计的AI工具，旨在通过符合反思性主题分析与逐字编码原则的结构化半自动化提示，支持归纳式主题分析。在"人机协作归纳式主题分析"框架指导下，本研究聚焦分析过程而非实质发现。三位经验丰富的质性研究者使用ITA-GPT辅助分析了加纳教师教育情境中教育研究的访谈转录文本。该工具支持熟悉数据、逐字实地编码、动名词描述性编码及主题开发，同时确保文本溯源性、覆盖度检验与可审计性。数据来源包括交互日志、AI生成表格、研究者修订记录、删除内容、插入内容、批注及反思备忘录。研究发现表明，ITA-GPT作为程序性支架构建了分析工作流程并增强了透明度，但解释权威始终由人类研究者掌握——他们通过修改、删除、否决、插入和批注等循环分析行为行使判断权。本研究揭示了如何通过负责任的人机协作实现归纳式主题分析。