The integration of AI language models, such as ChatGPT, into qualitative research has introduced new opportunities for efficiency and data processing. This study revisits a previous project on obesity prevention interventions, where in-depth interview data was analyzed to develop a causal loop diagram (CLD). By using ChatGPT to replicate the analysis, the researchers compared its results with their original approach. They found that ChatGPT contributes to improved efficiency and unbiased data processing, but also reveals limitations in context understanding. The findings suggest that AI tools have great potential as an augmentative tool rather than a replacement for human analysis in qualitative research. The study highlights the potential of AI in qualitative research, particularly in identifying feedback loops and enhancing data analysis. However, AI tools may lack the nuanced understanding and integration with broader academic discourses that human researchers bring. The study also notes that AI tools can introduce biases due to their training data, which may affect the analysis results. Ethical and data ownership concerns arise when using AI tools for research involving sensitive data. Despite these challenges, the study suggests that AI tools can be valuable in qualitative research, offering a more direct analysis of interview data. However, researchers should not rely solely on AI tools and should cross-check results with their own analysis. The study emphasizes the importance of transparency and the need for further research on the limitations and challenges of using AI in qualitative research. The study concludes that AI tools can serve as assistants, augmenting human capabilities to enhance the efficiency, thoroughness, and depth of qualitative research.The integration of AI language models, such as ChatGPT, into qualitative research has introduced new opportunities for efficiency and data processing. This study revisits a previous project on obesity prevention interventions, where in-depth interview data was analyzed to develop a causal loop diagram (CLD). By using ChatGPT to replicate the analysis, the researchers compared its results with their original approach. They found that ChatGPT contributes to improved efficiency and unbiased data processing, but also reveals limitations in context understanding. The findings suggest that AI tools have great potential as an augmentative tool rather than a replacement for human analysis in qualitative research. The study highlights the potential of AI in qualitative research, particularly in identifying feedback loops and enhancing data analysis. However, AI tools may lack the nuanced understanding and integration with broader academic discourses that human researchers bring. The study also notes that AI tools can introduce biases due to their training data, which may affect the analysis results. Ethical and data ownership concerns arise when using AI tools for research involving sensitive data. Despite these challenges, the study suggests that AI tools can be valuable in qualitative research, offering a more direct analysis of interview data. However, researchers should not rely solely on AI tools and should cross-check results with their own analysis. The study emphasizes the importance of transparency and the need for further research on the limitations and challenges of using AI in qualitative research. The study concludes that AI tools can serve as assistants, augmenting human capabilities to enhance the efficiency, thoroughness, and depth of qualitative research.