The use of generative AI and machine learning for qualitative data analysis

The emergence of generative AI in tertiary education has garnered significant scholarly attention. The widespread adoption of generative AI tools, exemplified by ChatGPT, amongst students has sparked debates concerning research integrity. However, the implementation of generative AI for data analysis has shown considerable promise in aiding students with the processing of extensive qualitative datasets. This review examines the merits and drawbacks of employing generative AI in qualitative investigations. To this end, a thorough examination of relevant scholarly works was undertaken. The review-encompassed studies that reported original findings were published in English between January 2018 and January 2025.  The following databases were searched: Scopus, ScienceDirect, EbscoHost, and ProQuest Central, using search terms such as "AI and Qualitative data," "Generative AI and qualitative research," and "Qualitative data analysis and generative AI." Twelve studies were included in the final review.  Of the twelve papers, seven explored the use of Gen AI in qualitative data analysis, three focused on the application of machine learning, and two assessed GEN AI and ML approaches in qualitative data analysis.   The SLR indicates that generative AI can serve as an invaluable asset for managing and synthesising substantial qualitative datasets, thus facilitating researchers' analytical processes. It is pertinent to note that certain qualitative data analysis software packages, including Nvivo and Atlas Ti, have already incorporated AI and machine learning capabilities. However, there remains an urgent necessity to explore strategies for maintaining the academic integrity of generative AI utilisation in qualitative research endeavours.