  Qualitative research, renowned for its in-depth exploration of complex
phenomena, often involves time-intensive analysis, particularly during the
coding stage. Existing software for qualitative evaluation frequently lacks
automatic coding capabilities, user-friendliness, and cost-effectiveness. The
advent of Large Language Models (LLMs) like GPT-3 and its successors marks a
transformative era for enhancing qualitative analysis. This paper introduces
QualiGPT, a tool developed to address the challenges associated with using
ChatGPT for qualitative analysis. Through a comparative analysis of traditional
manual coding and QualiGPT's performance on both simulated and real datasets,
incorporating both inductive and deductive coding approaches, we demonstrate
that QualiGPT significantly improves the qualitative analysis process. Our
findings show that QualiGPT enhances efficiency, transparency, and
accessibility in qualitative coding. The tool's performance was evaluated using
inter-rater reliability (IRR) measures, with results indicating substantial
agreement between human coders and QualiGPT in various coding scenarios. In
addition, we also discuss the implications of integrating AI into qualitative
research workflows and outline future directions for enhancing human-AI
collaboration in this field.
