AI and Qualitative Research: Proceed with Caution

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Author: Denis Avetisyan

Generative AI tools offer exciting possibilities, but their application to qualitative methods demands critical scrutiny.

This review examines the methodological risks associated with using generative AI in qualitative research, highlighting concerns about transparency, bias, and the validity of findings.

Despite growing enthusiasm for artificial intelligence across disciplines, its application to qualitative inquiry remains critically underexplored. This position paper, ‘Generative Artificial Intelligence in Qualitative Research Methods: Between Hype and Risks?’, critically assesses the methodological validity of employing generative AI in qualitative coding. We argue that current limitations in transparency, coupled with the potential for biased or inaccurate outputs, fundamentally undermine the rigor and trustworthiness essential to qualitative research. Given these substantial risks, can—and should—qualitative researchers prioritize methodological soundness over the allure of technological innovation?

Decoding the Qualitative Landscape

Qualitative research offers critical insight, yet increasing data complexity strains traditional analysis. Researchers face escalating demands on time and resources. Traditional coding, while rigorous, is time-intensive and susceptible to bias. Generative AI promises efficiency, but current systems fall short of meeting methodological requirements for validity and trustworthiness.

The Foundations of Trustworthy Inquiry

Methodological robustness underpins qualitative research, relying on dependability and confirmability to ensure findings are trustworthy. These principles assess consistency and logical coherence, differing across paradigms but agreeing on the need for a clear audit trail. Transparent coding is paramount, yet only 24% of AI-assisted studies provide full methodological documentation, hindering evaluation and replication.

Navigating the AI Minefield

Generative AI offers scalability, yet introduces risks to data integrity, including algorithmic bias and ‘hallucinations’. Data contamination—the homogenization of perspectives—threatens qualitative depth and nuanced understanding. Human-AI collaboration offers a viable path, though current AI systems don’t meet the standards for rigorous analysis.

Towards Responsible AI Integration

Commercial AI systems lack methodological transparency, hindering critical evaluation and raising concerns about accountability and bias. The EU AI Act represents a pivotal step towards responsible AI deployment, emphasizing risk assessment and ethical principles. Prioritizing transparency, fostering collaboration, and addressing bias are crucial for integrating AI into qualitative inquiry; diligent investigation is needed to reveal its true influence.

The pursuit of knowledge, as demonstrated by explorations into generative AI’s application within qualitative research, often necessitates a deliberate dismantling of established norms. This article highlights the inherent risks – the ‘hallucinations’ and biases – that surface when applying a ‘black box’ technology to nuanced interpretive work. As Marvin Minsky aptly stated, “The more we understand about how brains work, the more we realize how little we know.” This sentiment resonates with the article’s central argument; a cautious approach is paramount when integrating technologies lacking methodological transparency. The study isn’t about rejecting innovation, but about rigorously testing its foundations before accepting its outputs as valid insights.

Pushing the Boundaries—Or Just Falling Over Them?

The current anxieties surrounding generative AI in qualitative research aren’t about the tools themselves, but the impulse to treat outputs as unexamined truths. The discipline’s insistence on ‘meaning-making’ feels oddly fragile when confronted with an entity that simulates that process, regardless of grounding in lived experience. Future work isn’t about refining algorithms to reduce hallucinations or bias – those are symptoms, not the disease. The core problem is a willingness to bypass the laborious, messy process of iterative validation—to mistake plausible output for confirmability.

A more productive line of inquiry involves deliberately ‘breaking’ these systems. Not to expose flaws—though that’s inevitable—but to reverse-engineer the very logic of qualitative reasoning. If one can systematically prompt an AI to fail at tasks requiring contextual understanding, empathetic interpretation, or reflexive awareness, the points of divergence reveal precisely what constitutes human qualitative skill. The challenge isn’t building AI that does qualitative research, but defining, through its failures, what qualitative research is.

Ultimately, the limitations of generative AI may not reside in its inability to mimic human thought, but in its capacity to expose the implicit assumptions—and inherent vulnerabilities—within the qualitative tradition itself. The field must embrace this destabilization, not as a threat, but as an opportunity to rigorously re-examine its foundations, and rebuild them with a deeper understanding of the very processes it seeks to illuminate.

Original article: https://arxiv.org/pdf/2511.08461.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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2025-11-12 15:10