We Reject the Use of Generative Artificial Intelligence for Reflexive Qualitative Research

Abstract

Four hundred and nineteen experienced qualitative researchers from 32 countries invite readers of Qualitative Inquiry to consider their position on use of generative artificial intelligence (GenAI) for qualitative research. We hold the position that analytic approaches such as reflexive thematic analysis are human research practices requiring a subjective, positioned, and reflexive researcher and therefore the use of GenAI in such approaches is not methodologically congruent. We additionally reject GenAI for reflexive qualitative approaches on the grounds of social and environmental justice.

We write as 419 experienced qualitative researchers from 32 countries, to reject the use of generative artificial intelligence (GenAI) applications for Big Q Qualitative approaches (Kidder & Fine, 1987), such as reflexive thematic analysis, or various phenomenological approaches. Reflexive thematic analysis is a method undertaken by human researchers for analyzing qualitative data by developing, interpreting, and reflecting on patterns of meaning (themes). This analytical process is deeply subjective and iterative and mindful of power relations (Braun & Clarke, 2019). Phenomenological, anthropological, ethnographic, discourse, and other reflexive qualitative approaches are similarly interpretive, subjective, and mindful (Brewer, 2000; Fabian, 2014; Johnstone & Andrus, 2024; Moran, 2002). Our rejection of GenAI is grounded in both methodological and ethical concerns. We urge qualitative researchers to think critically about—and reject—the use of GenAI in such analyses. The primary reasons we note for such rejection relate to:

1. GenAI as simulated intelligence is incapable of meaning-making;

2. Qualitative research should remain a distinctly human practice;

3. The established manifold harms of GenAI, especially to the environment and workers in the Global South.

First, GenAI remains simulated intelligence only, based on statistical predictive algorithms without any understanding of the world, or the meaning of the language that constitutes the data being analyzed, or indeed the meaning of the resulting themes produced when simulating qualitative analysis. While GenAI with human involvement might be able to produce something that superficially resembles reflexive qualitative analysis (through a simulation of the methodological process), it cannot be reflexive, because, by definition, reflexive qualitative analysis is an inherently meaning-based technique. Just as the meaning-based requirement of reflexive thematic analysis, for example, distinguishes it methodologically from word-counting techniques such as content analysis (which can be automated), so too it must also exclude GenAI on the basis that GenAI is fundamentally incapable of genuinely making meaning from language (Webster, 2025). Failure to recognize these limitations of GenAI risks analyses that reinforce dominant paradigms and biases. That is, the algorithmic patterns upon which GenAI operates predisposes GenAI to identify, replicate, and reinforce dominant language and patterns; risking the further quieting of marginal voices and practices, including those of critical scholars. The voices and practices of people who live/breathe/feel/imagine/construct knowledge in the maroons of life—along with their stunning/quirky/complex/unpredictable ways—may be lost or worse; sacrificed.

Second, reflexive qualitative research is a distinctly human practice, undertaken by humans, with or about humans (e.g., through interviews, focus groups or textual data), and for the benefit of humans. The central tenet of social science research is to more deeply understand people and social processes, and to explore and interrogate meaning-making. Researchers often do this through connecting with and observing social others. While some researchers suggest that GenAI-supported qualitative analyses are helpful, so long as a human is included in the analytical “loop,” they also warn that our desire for GenAI to be reliable reduces our capacity to critically appraise GenAI outputs (Gamieldien et al., 2023; Lixandru, 2024; Törnberg, 2024; Xiao et al., 2023). Others emphasize that uncritical use of GenAI introduces epistemic risks to the interpretive meaning-making core of qualitative research (Nguyen & Welch, 2025). We hold the position that only a human can undertake reflexive qualitative analytical work, and therefore, use of GenAI is inappropriate in all phases of reflexive qualitative analysis, including initial coding. Researchers must anchor the process of making strong psychodynamic interpretations in their own humanity.

Third, we draw your attention to the concerning exploitative, colonialist, and extractivist practices in which big AI corporations engage, which have harmful impacts on humans and the planet due to exposure to electronic waste and the increased use of water and energy, land clearing, devastation of habits and greenhouse gas emissions, by the data centers being built to service GenAI expansion. We are concerned about these serious ethical and health issues. As qualitative researchers concerned with social justice, and bound by ethical obligations to minimize harm, we note that several prominent researchers have raised concerns about the negative impact of increased use of GenAI both on our environment and on fellow humans. Critics have pointed to the extractivist, racist, imperialist, and exploitative ethos motivating Big AI Tech in their quest for profit (Brennan et al., 2025; Hanna & Bender, 2024; Mejias & Couldry, 2024; Tacheva & Ramasubramanian, 2023), and which is transforming epistemic agency in higher education (Lindebaum et al., 2025). For example, Galaz and colleagues show that AI has rapid and extensive uptake in multiple industries including farming, forestry, aquaculture, and—ironically—climate change. Yet this uptake poses significant harms, such as AI-bias-driven increased inequity and food insecurity, cascading failures, and AI-driven irreversible changes in ecosystems (Galaz et al., 2021). The GenAI boom is accompanied by the expansion of massive infrastructural components to support it, including data centers and under-sea cabling (Hogan, 2024; Wang et al., 2024). These infrastructures expose humans and other elements of ecosystems to significant habitat disruption and environmental hazards from land clearing, deep sea tunneling, greenhouse gas emissions, and impacts caused by its water and energy use (Hosseini et al., 2025; Lupton, 2025; Osmanlliu et al., 2025). Another way in which GenAI poses considerable harm to human health is through exploitation of workers working on training or moderating digital data content. Researchers have identified the psychological effects on AI data workers in the Majority World who are tasked with helping train large language models to detect and filter toxic content (Mejias & Couldry, 2024; Tacheva & Ramasubramanian, 2023).

While this third point is more concerned with ethical objections rather than a methodological concern, we see them as interconnected and warn against ignoring these complex negative impacts of our choices on others, especially in light of points 1 and 2.

For these reasons, we reject GenAI for reflexive thematic analysis and other reflexive qualitative approaches.

Signed,

Tanisha Jowsey, Virginia Braun, Victoria Clarke, Deborah Lupton, Michelle Fine; and endorsers ¹ : Gareth J. Treharne, Deborah L. Tolman, Cathy Banwell, Neera R. Jain, Megan E. L. Brown, Duc C. Nguyen, Elizabeth Sturgiss, Francesca A. Williamson, Patricia Thille, Gareth Terry, Catherine Welch, Joep Cornelissen, Martina Kelly, Robyn Woodward-Kron, Janine Wiles, Peter Adams, Nikki Hayfield, Tine Köhler, Antonia Lyons, Bill Harley, Ann L Cunliffe, Paul Hibbert, Leslie Swartz, Peter Hegarty, Catriona Ida Macleod, Lauren J. Breen, Christina Lee, Fiona Grattan, Irmgard Tischner, Caroline Jagoe, Jo Brooks, Myriam Pannard, Robbie Busch, Brett Scholz, Priscilla Boshoff, Julie Spray, Lorelle Dismore, Gareth Davey, Morten Skovdal, Liezille Jacobs, Mick Finlay, Chris Noone, Ryan L. McGrath, Maxine Woolhouse, Rahul Sambaraju, Muhamad Alif Bin Ibrahim, Joanna Semlyen, Gavin B. Sullivan, Carla Willig, Viola Sallay, Petra Boynton, Elizabeth Peel, Nicola Jearey-Graham, Lesley Storey, Kara Allen, Jenny Cole, Eman AlBedah, Dirk Lindebaum, Monika Stelzl, Sarah Seymour-Smith, József Rácz, Ruth Walker, Bachir Sirois-Moumni, Jehannine Austin, Ea Høg Utoft, Benjamin T. Sharpe, Garcia Ashdown-Franks, Stuart McClean, Sarah Kiden, Annelies Kleinherenbrink, Katerina Litsou, Nafis Mahmud Khan, Minja Axelsson, Sara E. Grummert, Aine Carroll, Luke Fletcher, Tasha Wainstein, Bethany Thomas, Andrea Whittle, Sarah Bell, Vee Copeland, Sarah Esegbona-Adeigbe, Sadie Rockliffe, Nessa Millet, Michelle Y. Martin Romero, Ariel Ducey, Sarah Kirby, Abigail Locke, Eileen Joy, Nikita Hayden, Rebecca A Szabo, Michele Jarldorn, Sibusiso Mkwananzi, Daniel Cezar Da Cruz, Tharin Phenwan, Francisco José Eiroá Orosa, Lenandlar Singh, Caroline L Roberts, Animesh Paul, Ayşegül Bakır, Randi Spiker, Serena Daalmans, Katrina McChesney, Samantha Thomas, Emma Tennent, Sara McClelland, Olivier Jutel, Valerie Cooper, Pieta Shakes, Ben Harris-Roxas, Jenna L Gillett, Georgia Pavlopoulou, Caroline Lenette, Rosemary Overell, Elif Lootens, Jade Le Grice, Katie B. Sullivan, Karena J. 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Stewart, Hugh Gorick, Kate Swainston, Lauren McCarthy, Victoria Ruby-Granger, Allison Ruth Dunne, Michelle N Lafrance, Meg Mundell, Nicola Boydell, Rosemary Chigevenga, Peter Branney, Alison Torn, Christiaan Oostdijk, Javier Monforte, Suzanne Scott, Victoria J Palmer, Bethan Jones, Iain Thomas Killoughery, Emma Christensen, Candice Whitaker, Brandon Sabourin, Elaine Keane, Fiona Spotswood, Janne Tienari, Afrodita Marcu, Blessing Marandure, Caroline Huxley, Renée E. Stalmeijer, Chase Ledin, Marie Joachim, Priya Kumar, Samantha Flynn, Bregje de Kok, Paul Reilly, Carol A. Jasper, Helen Grace Concepcion Q. Fernandez, Philip Lew Chun Foong, Donne Jone Panizales Sodusta, Michael Larkin, Helen Clegg, Jenni Ward, Aliya Sarsenbayeva, Andrea Werner, Aidan A. Sunassee, Zaeem Ul Haq, Gloria Fraser, Coralie Mercerat, Emma Davies, Rea Daellenbach, Mirela Zaneva, William J. Robertson, Katrina Roen, Kathryn Hicks, Michelle Salmona, Loene Howes, Dean Pierides, Edward Maclin, Amanda Lee Savage, Renee Fiolet, Elisa Calvo, Sally Sargeant, Carla Treloar, Stephenie Pagoudis, Samantha Newell, Peta Callaghan, Kristi Urry, Marianne Clausen, Anna Klas, Matthew I. Mackay, Jessica L. Mackelprang, Alice-Viviana Bercean, Aaron Koay, Blake Quinney, Jayne Leonard, Calvin C. Fernandez, Mia Majstorovic, Madeline Heygers, Stephen Goulding, Maggie O’Neill, Kym M. McCormick, Stefanie Reissner, Will Day, Heather Sutherland, Christopher J Graham, Clair Hebron, Mark Warner, Dagmar Monett, Miriam Reynoldson, Sinéad Sheehan, Justine Anthony, Kathryn Conrad, Ismail Ahmad Shaikh, Donatella Di Marco, Paula Corcoran, Madeline Nase, Jane Healy, Amrit Chauhan, Lise Lafferty, Anshu Chaudhary, Alys Griffiths, Emily Allen Paine, Richard Longman, Thomas M. Leeder, Emily Arden-Close, Emily Arden-Close, Sophia Mavridi, Carlos Azevedo, Maria José Sá, Robyn Delbridge, Thomas Byers, Robbie Fordyce, Genia Bettencourt, Auzi Asfarian, Danielle Heinrichs Henry, Danielle Heinrichs Henry, Melissa J. Rogerson, Octavia Calder-Dawe, Lucy Sparrow, Emma Turley, Brian Law, Bridget Conor, Mahli-Ann Butt, Xavier Ho, Neil Munro, Hugh Davies, Bingyi Han, Amy Chandler, Sara Diogo, Ian Couper, Ola Demkowicz, Emma McLorie, Lindsey Pope, Andrew Whitworth, Annie O’Brien, Jo Hickman Dunne, Lucy Prodgers, Sergio A. Silverio, Suzanne Hamilton, Amy Grove, Amy M. Russell, Kathryn Almack, Jade Parnell, Myrofora Kakoulidou, Katherine Knighting, Stan Papoulias, Lauren E. 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Supplemental Material

sj-pdf-1-qix-10.1177_10778004251401851 – Supplemental material for We Reject the Use of Generative Artificial Intelligence for Reflexive Qualitative Research

Supplemental material, sj-pdf-1-qix-10.1177_10778004251401851 for We Reject the Use of Generative Artificial Intelligence for Reflexive Qualitative Research by Tanisha Jowsey, Virginia Braun, Victoria Clarke, Deborah Lupton and Michelle Fine in Qualitative Inquiry