Using ChatGPT for thematic analysis of qualitative interviews in cultural research: a methodological investigation

OBJECTIVE: The use of artificial intelligence (AI) tools in clinical and psychotherapy research is gaining increasing attention. This study explores the application of a large language model (LLM), ChatGPT version 4.5, as a coding assistant for qualitative data in psychotherapy research. METHODS: Twenty-four semi-structured qualitative interviews were conducted with 12 patients with bipolar disorder and 12 family caregivers participating in a study of a culturally adapted psychotherapy intervention in Pakistan. Interviews were transcribed in Roman Urdu and analyzed using ChatGPT, which was prompted to generate English-language thematic codes and interpretations. Outputs were critically reviewed by a bilingual qualitative researcher for cultural relevance and semantic accuracy. RESULTS: ChatGPT produced efficient, structured codes and surface-level thematic interpretations. However, it frequently missed emotional nuances, idiomatic expressions, and culturally embedded meanings. Human oversight was essential for ensuring interpretive depth and contextual validity. CONCLUSION: The use of ChatGPT as an AI-assisted tool for qualitative analysis offers potential for enhancing efficiency in psychotherapy research but remains limited in capturing cultural and emotional subtleties. AI-human collaboration represents a promising but still evolving approach for cross-cultural qualitative research in mental health.