A Practical Guide and Assessment on Using ChatGPT to Conduct Grounded Theory: Tutorial

Generative large language models (LLMs), such as ChatGPT, have significant potential for qualitative data analysis. This paper aims to provide an early insight into how LLMs can enhance the efficiency of text coding and qualitative analysis, and evaluate their reliability. Using a dataset of semistructured interviews with blind gamers, this study provides a step-by-step tutorial on applying ChatGPT 4-Turbo to the grounded theory approach. The performance of ChatGPT 4-Turbo is evaluated by comparing its coding results with manual coding results assisted by qualitative analysis software. The results revealed that ChatGPT 4-Turbo and manual coding methods exhibited reliability in many aspects. The application of ChatGPT 4-Turbo in grounded theory enhanced the efficiency and diversity of coding and updated the overall grounded theory process. Compared with manual coding, ChatGPT showed shortcomings in depth, context, connections, and coding organization. Limitations and recommendations for applying artificial intelligence in qualitative research were also discussed.