Clarifying the debate over AI in qualitative research
In this post, I focus on the spaces and possibilities between the two extremes of the debate, arguing that AI in various forms can be integrated more thoughtfully and more responsibly at key moments during the research process, perhaps in less obvious ways than what is suggested by each extreme side in this debate.
- Artificial Intelligence
- qualitative research
By Evangelia BerdouJuly 7, 2026
Key takeaways
- The discussion on using AI in qualitative research often pits two unhelpful approaches against each other: a total ban vs. unfettered AI automation.
- AI tools add value in small, targeted ways. They can improve workflows, support better research design, and help explore and validate evidence.
- Ultimately, impact depends on how improvements in efficiency are used. AI can deepen research insights or just increase output, shaped by incentives and client demands.
The debate on the use of AI in qualitative research is often framed by two extreme positions.
On the one side are the methodological purists. This group includes the scholars who, in a recent open letter to the Qualitative Inquiry journal, argued that generative AI has no place in certain forms of qualitative research, as it is unable to generate meaningful insight—its outputs are statistical predictions, untouched by context or experience.
On the other side are those advocating for automation at points throughout the research process—from data collection and analysis to reporting. This side is more diverse in its makeup and background, comprising professionals who commission and/or develop AI solutions for qualitative research yet may have little understanding of what such research is and entails. It also includes some applied researchers with social science backgrounds using AI due to lack of resources and/or narrow client expectations/requirements.
This all-or-nothing framing is not helpful. A total ban on the use of AI in qualitative research is unrealistic. Given the rapid spread of AI tools, soon that may be like asking someone not to use the Internet. But neither should researchers be using AI tools indiscriminately. These are important issues for food system and global development research, which stand to benefit from some forms of AI automation and also face tenuous funding.
In this post, I focus on the spaces and possibilities between the two extremes of the debate, arguing that AI in various forms can be integrated more thoughtfully and more responsibly at key moments during the research process, perhaps in less obvious ways than what is suggested by each extreme side in this debate.
It is also important to acknowledge that discussions on the role of AI in qualitative research are not only about analytical rigor and that decisions on if and how to use AI are not something that researchers alone can make.