Advantages, limitations, and ethical concerns of AI-assisted qualitative research in nursing: insights from a human–AI comparative thematic analysis
TL;DR
In this study, AI-assisted qualitative analysis was useful as a supportive tool for preliminary thematic exploration and data organization, but human reflexive engagement remained essential for contextual interpretation, ethical judgment, and the representation of nursing experiences.
Abstract
The rapid development of large language models has accelerated the use of artificial intelligence (AI) in qualitative research. AI-assisted thematic analysis may improve coding efficiency and data organization, but concerns remain about its ability to interpret emotionally nuanced, contextually embedded, and culturally situated nursing narratives. Existing studies have emphasized coding performance and efficiency, whereas ethical and interpretive concerns in nursing qualitative research remain insufficiently examined. This secondary qualitative analysis compared human-led reflexive thematic analysis and ChatGPT-assisted thematic analysis of the same nursing interview dataset and explored the advantages, limitations, and ethical concerns of AI-assisted qualitative inquiry in nursing. Semi-structured interview data originally collected within a broader nursing research platform project were analyzed through two separate pathways: human-led reflexive thematic analysis based on Braun and Clarke’s approach and ChatGPT-assisted thematic analysis. The research team compared the outputs to examine convergence, divergence, contextual simplification, thematic representation, and ethical implications. Ethical concerns were interpreted through comparative and reflexive analysis. The research team observed that AI-assisted analysis supported rapid data organization, preliminary coding, and identification of explicit thematic patterns. However, the two analytic pathways differed in their representation of contextual meanings, tacit nursing knowledge, and experience-based nursing narratives. Through comparative and reflexive interpretation, four ethical themes were identified: privacy and data security concerns; loss of contextual and emotional understanding; algorithmic reductionism and cultural bias; and changing roles and responsibilities in nursing research. AI-assisted outputs tended to be semantically coherent but contextually simplified. In this study, AI-assisted qualitative analysis was useful as a supportive tool for preliminary thematic exploration and data organization. However, human reflexive engagement remained essential for contextual interpretation, ethical judgment, and the representation of nursing experiences. The findings suggest that ethical concerns extend beyond technical performance to contextual representation, interpretive integrity, and researcher responsibility. Future research should develop reflexive, ethically grounded human–AI collaborative approaches to qualitative nursing inquiry. Not applicable.