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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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.
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 . . .
Ars Technica's policy on generative AI
How Ars Technica uses, and doesn't use, generative AI.
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...
Qualitative research: standards, challenges, and guidelines
Qualitative research methods could help us to improve our understanding of medicine. Rather than thinking of qualitative and quantitative strategies as incompatible, they should be seen as complementary. Although procedures for textual interpretation differ from those of statistical analysis, because of the different type of data used and questions to be answered, the underlying principles are much the same. In this article I propose relevance, validity, and reflexivity as overall standards for qualitative inquiry.
Use of generative artificial intelligence | ÉPICBiodiversity
An alternative version of this document was initially drafted by Timothée Poisot with input from members of the Viral Emergence Research Initiative, and further revised based on a conversation with group members. For this reason, it is excluded from the CC BY-NC-SA license under which the rest of the website is published, and may not be reproduced without permission.
Charting AI’s Role in Scientific Discovery — Renaissance Philanthropy – A brighter future for all through science, technology, and innovation
Renaissance Philanthropy, with support from Google.org , is conducting a landscape study of AI integration in scientific research — and we want your perspective.
[Keynote 01] A Theory of Appropriateness: Social Norms for Humans and AIs
Identity, positionality and reflexivity: relevance and application to research paramedics
This article introduces the reader to the concepts of identity, positionality and reflexivity and outlines their relevance to research paramedics. We outline how a researcher’s identity and positionality can influence all aspects of research, including the research question, study design, data collection and data analysis. We discuss that the ‘insider’ position of paramedics conducting research with other paramedics or within their specific clinical setting has considerable benefits to participant access, understanding of data and dissemination, while highlighting the difficulties of role duality and power dynamics. While positionality is concerned with the researcher clearly stating their assumptions relating to the research topic, the research design, context and process, as well as the research participants; reflexivity involves the researcher questioning their assumptions and finding strategies to address these. The researcher must reflect upon the way the research is carried out and explain to the reader how they moved through the research processes to reach certain conclusions, with the aim of producing a trustworthy and honest account of the research. Throughout this article, we provide examples of how these concepts have been considered and applied by a research paramedic while conducting their PhD research studies within a pre-hospital setting, to illustrate how they can be applied practically.