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 Reject the Use of Generative Artificial Intelligence for Reflexive Qualitative Research
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.
AI is turning research into a scientific monoculture
Generative AI deserves scientific attention. But the rush to study it is producing a feedback loop of topical and methodological convergence, flattening scientific imagination and crowding out the pluralism needed to keep research adaptive, resilient, and intellectually generative.
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Where does the rigor go? Research software and the future of trustworthy science.
Generative AI now makes it dramatically easier to produce something that looks like research: analysis code, figures, literature reviews, even whole pap…
Reflexive discourse analysis: A methodology for the practice of reflexivity
How to implement reflexivity in practice? Can the knowledge we produce be emancipatory when our discourses recursively originate in the world we aim to challenge? Critical International Relations (IR) scholars have successfully put reflexivity on the agenda based on the theoretical premise that discourse and knowledge play a socio-political role. However, academics often find themselves at a loss when it comes to implementing reflexivity due to the lack of adapted methodological and pedagogical material. This article shifts reflexivity from meta-reflections on the situatedness of research into a distinctive practice of research and writing that can be learned and taught alongside other research practices. To do so, I develop a methodology based on discourse: reflexive discourse analysis (RDA). Based on the discourse analysis of our own discourse and self-resocialisation, RDA aims to reflexively assess and transform our socio-discursive engagement with the world, so as to render it consistent with our intentional socio-political objectives. RDA builds upon a theoretical framework integrating discourse theory to Bourdieu’s conceptual apparatus for reflexivity and practices illustrated in the works of Comte and La Boétie. To illustrate this methodology, I used this very article as a recursive performance. I show how RDA enabled me to identify implicit discriminative mechanisms within my discourse and transform them into an alternative based on love, to produce an article more in line with my socio-political objectives. Overall, this article turns reflexivity into a critical methodology for social change and demonstrates how to integrate criticality methodologically into research and writing.
Qualitative Research in an Era of Artificial Intelligence: A Pragmatic Approach to Data Analysis, Workflow, and Computation
Computational developments—particularly artificial intelligence—are reshaping social scientific research and raising new questions for in-depth methods such as ethnography and qualitative interviewing. Building on classic debates about computers in qualitative data analysis, we revisit possibilities and dangers in an era of automation, large language model chatbots, and big data. We introduce a typology of contemporary approaches to using computers in qualitative research: streamlining workflows, scaling up projects, hybrid analytical methods, the sociology of computation, and technological rejection. Drawing from scaled team ethnographies and solo research integrating computational social science alongside in-depth observation, we describe methodological choices across study life cycles, from literature reviews through data collection, coding, text retrieval, and representation. We argue that new technologies hold potential to address long-standing methodological challenges when deployed with knowledge, purpose, and ethical commitment. Yet, a pragmatic approach—moving beyond technological optimism and dismissal—is essential given rapidly changing tools that are both generative and dangerous. Computation now saturates research infrastructure, from algorithmic literature searches to scholarly metrics, making computational literacy a core methodological competence in and beyond sociology. We conclude that when used carefully and transparently, contemporary computational tools can meaningfully expand, rather than displace, the irreducible insights of qualitative research.
Conducting a Qualitative Document Analysis
Document analysis has been an underused approach to qualitative research. This approach can be valuable for various reasons. When used to analyze pre-existing texts, this method allows researchers to conduct studies they might otherwise not be able to complete. Some researchers may not have the resources or time needed to do field research. Although videoconferencing technology and other types of software can be used to reduce some of the obstacles qualitative researchers sometimes encounter, these tools are associated with various problems. Participants might be unskillful in using technology or may not be able to afford it. Conducting a document analysis can also reduce some of the ethical concerns associated with other qualitative methods. Since document analysis is a valuable research method, one would expect to find a wide variety of literature on this topic. Unfortunately, the literature on documentary research is scant. This paper is designed to close the gap in the literature on conducting a qualitative document analysis by focusing on the advantages and limitations of using documents as a source of data and providing strategies for selecting documents. It also offers reasons for using reflexive thematic analysis and includes a hypothetical example of how a researcher might conduct a document analysis.
Ideation with Generative AI—in Consumer Research and Beyond
Abstract The use of generative AI (genAI) in consumer research is rapidly evolving, with applications including synthetic data generation, data analysis, and more. However, their role in creative ideation—a cornerstone of consumer research—remains underexplored. Drawing on the human creativity literature, we propose that ideation with genAI is facilitated by its productivity and semantic breadth, which are psychologically analogous to the dual pathways of persistence and flexibility in human ideation. Further, we distinguish between the utility of genAI as a key ideator versus humans as key ideator, conceptualized through the genAI ideation roles of Designer and Writer and of Interviewer and Actor. While genAI excels in generating incremental improvements, its potential for groundbreaking innovation could be unlocked by leveraging its ability to prompt human creativity. This article advances the theoretical and practical understanding of genAI in ideation for consumer research, offering numerous practical guidelines for integrating generative AI into research while emphasizing human–AI collaboration to achieve radical insights.
Field Theory: AI as Social Science Question, Object & Tool
Uses of advanced artificial intelligence are changing how societies organize labor, govern, produce knowledge, and make meaning. In light of these developments, this essay argues that AI models, tools, and systems pose three interrelated imperatives for social science: they demand renewed attention to social theories of how technology, human experience, and social order are entangled; they require study as objects of inquiry in their own right; and they offer capabilities that may transform—or upend—the practice of social investigation itself. From Weber’s analysis of rationalization to Du Bois’s study of technology and inequality to contemporary scholarship on algorithmic governance, the essay examines what social science distinctively offers: the capacity to historicize the apparently unprecedented, to trace connections across scales, and to center those most affected by technological change. It identifies how algorithmic systems are remaking the distribution of opportunity and risk as a central task of social inquiry and asks what futures social science might help bring into being.
Human perspectives on AI · Global Voices
This GV Spotlight edition will explore how the use of, promotion of, and resistance to artificial intelligence is playing out for the Global Majority.
Generative AI for Pro-Democracy Platforms · From Novel Chemicals to Opera
Online discourse faces challenges in facilitating substantive and productive political conversations. Recent technologies have explored the potential of generative AI to promote civil discourse, encourage the development of mutual understanding in a discussion, produce . . .
Social Physics in the Age of Generative AI: How, Exactly, Is Society Complex?
The aspiration for a social physics — a science that would reveal universal laws of social life analogous to those governing the natural world—has haunted the social sciences since Comte. Each new wave of quantitative and computational methods breathes fresh life into this vision. The current moment is no exception: advances in artificial intelligence and large language models have reinvigorated attempts to model society through the methods of social physics. This essay argues that these ambitions rest on a confusion about the nature of social complexity. Drawing a distinction between systems complexity and anthropological complexity, we identify the limitations of analyzing society as a complex system akin to natural systems. One crucial obstacle for social physics is the analysis of meaning and interpretation. Generative artificial intelligence (GenAI) offers important opportunities to model language and reflexivity, inviting and perhaps compelling social physics to confront meaning and interpretation as constitutive features of the social world. At the same time, GenAI leaves untouched and may even aggravate social physics’ failure to grasp power, institutions, and inequality.
Ars Technica's policy on generative AI
How Ars Technica uses, and doesn't use, generative AI.
Guest Post - The Perils of Using Generative AI to Perform Research Tasks: Editors’ and Publishers’ Viewpoints - The Scholarly Kitchen
Today's guest post offers a review of a panel of publishers and editors discussing the pros and cons of using Generative AI, along with ethical and policy implications.
Generative artificial intelligence: The Times and Sunday Times editorial guidelines
As we explore the potential of generative artificial intelligence our commitment is to uphold our high editorial standards.
Is replication <i>possible</i> in qualitative research? A response to Makel et al. (2022)
There has been much debate in recent years about how open research practices, which have been promoted in efforts to improve research robustness, may (not) be appropriate for qualitative methodolog...
