ChatGPT for Qualitative Data Analysis: Hype or Help?

The introduction of generative artificial intelligence technologies such as ChatGPT has opened up new opportunities and problems for qualitative research, which has traditionally relied on human-centered interpretation and reflexivity. This study investigates if ChatGPT is a genuinely useful tool or if its increasing popularity is a result of technological hype. Drawing on current case studies, scholarly discussions, and early-stage experimental research, this study analyzes ChatGPT's role in helping qualitative data analysis procedures, specifically coding, theme creation, transcript summary, and unstructured text exploration. While some researchers report increased efficiency and analytical support especially for large datasets or early-stage theme identification others highlight concerns about ChatGPT’s lack of contextual sensitivity, the opacity of its decision-making processes, and the potential for misrepresenting complex human narratives. Using a conceptual and exploratory approach, the study synthesizes emerging practices and offers a critical reflection on how, when, and to what extent AI tools like ChatGPT can be responsibly integrated into qualitative workflows. Key ethical and methodological concerns are addressed, including researcher dependence, data privacy, and the potential erosion of interpretive depth. Rather than advocating for wholesale adoption or rejection, the study proposes a framework for using ChatGPT as a complementary analytical assistant—one that can enhance but not replace human insight. The findings underscore the need for transparency, reflexivity, and alignment with qualitative epistemologies when deploying AI in research settings. This paper contributes to the ongoing discourse on AI’s role in social science methodologies and aims to support researchers in navigating this evolving landscape with critical awareness.