AI and Qualitative Research: From Research Tool to Methodological Challenge Artificial intelligence is no longer a polite “future guest” waiting outside the research office; in this article, it has already walked into healthcare, education, and scientific inquiry. Dellafiore, Saba, Collaro, and Artioli open by making the background unmistakably clear: AI, as a field spanning computer science, engineering, mathematics, linguistics, and psychology, is rapidly reshaping research

LLMs can produce many qualitative codes, but not all of them carry meaning. This article looks at what Gemma2 and Llama3.1 actually add to thematic analysis.

AI Agents in Online Surveys If you run online surveys today, there is a good chance your respondents come from an online recruitment platform. These platforms now sit behind a large share of behavioral research, shaping academic work in psychology, public health, economics, and political science. They also support consequential commercial decisions in product design and public communication. But the rise of large language models (LLMs) has made an old concern feel newly urg

Why GATOS Matters Qualitative data are often where social scientists go when numbers are not enough. Open-ended survey responses, interviews, and written reflections can capture forms of meaning that are difficult to reduce to numbers. As Saldaña (2011) argues, qualitative data analysis is fundamentally a process of meaning-making: researchers construct patterns, identify relationships, and move between inductive and deductive forms of reasoning. The difficulty is that this

Paradigm Shift from Traditional Coding to Conversational Analysis The rapid emergence of generative artificial intelligence (genAI) tools is fundamentally reshaping how researchers engage with unstructured data, particularly within the domain of qualitative analysis. While these technological advancements offer exciting possibilities, they also bring significant challenges. Traditional qualitative coding methods, though rigorous, are often extremely time-intensive, whereas ge

AI in Qualitative Research: Collaborative Partner or Epistemic Risk? The rise of Generative Artificial Intelligence (GenAI) has sparked a profound scholarly debate in the field of qualitative research, centered on a critical question: Do Large Language Models (LLMs) enhance the depth of qualitative exploration, or do they fundamentally threaten its epistemological foundations? Adam S. Hayes (2025) argues that LLMs are revolutionizing how scholars work with textual data by all

Unpacking Bias in AI in Qualitative Research GenAI Is More Than a Tool Looking back from the mid-2020s, it is hard to think of any recent change in academia more disruptive than the rise of generative artificial intelligence (GenAI). As Deleuze and Guattari wrote in the 1980s, “Tools exist only in relation to the interminglings they make possible or that make them possible.” Today, this observation feels especially relevant to new AI technologies, and particularly to GenAI. G

Human–AI Delegation in Social Science Codifiable Tasks vs. Tacit Knowledge, Theoretical Originality, and Field Judgment In an age where everyone is talking about AI, social science research finds itself at a delicate crossroads. Recently, Yongjun Zhang (2026) from Stony Brook University raised a provocative question in his latest paper, "Vibe Researching as Wolf Coming": what parts of your research can only you do, and what can the machines never replace? Is it running tediou

Generative AI democratization in consumer research: broader participation brings new voices into the data ecosystem. Today, a consumer researcher beginning a new project may start not with a database, but with a generative AI system. Within seconds, a structured framework emerges. Literature streams are synthesized, research questions are articulated, and potential hypotheses are outlined. The efficiency is undeniable. Yet Huang and Rust argue that efficiency is not the centr

How GenAI Transforms Qualitative Data Analysis Generative Artificial Intelligence (GenAI) is reshaping qualitative consumer culture research by introducing new efficiencies while raising epistemological tradeoffs across theoretical, embodied, empirical, and historical dimensions. In other words, when consumer culture researchers upload interview transcripts, fieldnotes, or archival materials into large language models (LLMs), what changes is not merely research efficiency. Ep

The rapid development of generative artificial intelligence is redefining how consumer and marketing researches are conducted. When a model can replicate the consumer research processes of 35 top-tier journal articles within minutes, are we still engaging in “human-led science”? A study by Yoo, Haenlein, and Hewett, published in the Journal of the Academy of Marketing Science, addresses precisely this question: Is AI merely transforming research tools, or is it reshaping rese

The contrast between human interpretive work and computational code reflects the tension between qualitative meaning-making and generative AI’s promise of efficiency Generative AI is increasingly used in qualitative research, yet its promise of efficiency raises fundamental questions about interpretation, meaning-making, and the role of the researcher. This article is based on the academic presentation "Interpreting Qualitative Data with AI: Pitfalls and Potential" by Duc Ngu

Economics in the Age of AI Agents Over the past two years, the way economists conduct research has undergone a profound structural transformation. In his 2025 NBER Working Paper AI Agents for Economic Research, Anton Korinek argues that the research community is moving from simple conversational systems such as ChatGPT to a new paradigm centered on autonomous AI agents. These systems integrate text generation with planning, memory, and tool use, enabling them to execute multi

AI and qualitative research As generative artificial intelligence (GenAI) rapidly transforms the landscape of scientific research, qualitative scholars are confronted with an unprecedented technological challenge: Can this emerging tool—powered by large language models (LLMs)—truly fulfill the humanistic task of qualitative data analysis, which is fundamentally rooted in interpretive work? In their recent publication, Nguyen and Welch (2025) offer a systematic and incisive re

AI creativity compared to human imagination: while large language models generate patterns based on training data, human creativity draws from unique experiences and emotions. Setting the Stage: AI and Creativity Large language models (LLMs) are advancing at a remarkable speed. From writing stories and poetry to brainstorming ideas, their presence in creative work is undeniable. But here lies a fundamental question: can these models genuinely support collective creativity, or

Artificial intelligence (AI) is transforming the landscape of academic research at an astonishing speed. From idea generation to data production, from writing to journal publishing, AI is infiltrating every link of the academic ecosystem (Van Quaquebeke et al., 2025). In this article published in The Leadership Quarterly, Van Quaquebeke, Tonidandel, and Banks point out that we are already in an era where AI deeply participates in the production of knowledge. The real question

Introduction Generative AI is profoundly reshaping consumer research, enhancing creative ideation, supporting qualitative analysis, and redefining the field’s trajectory. Three empirical studies and one review from the Journal of Consumer Research, Oxford Academic, offer complementary frameworks and practical guidance, revealing the vast potential and pitfalls of human–AI collaboration. Creative Ideation: The Double-Edged Sword of LLMs As a cornerstone of consumer research, c

Artificial intelligence (AI), particularly generative AI as applied in content creation, has become a research topic of significant interest. While discussions about its quality, integrity, and copyright issues in research have arisen, the potential of generative AI to assist researchers in developing experimental stimuli is often overlooked. van Berlo et al. (2024) introduced the MADE (Mapping, Assembling, Demonstrating, Executing) framework, aiming to provide a comprehensiv

Since its emergence in December 2022, ChatGPT has sparked widespread discussion, leading to the rapid adoption of Large Language Models (LLMs) across various industries. These models now assist users in handling a broad spectrum of open-ended tasks, many of which demand a high level of creativity. Last year, Zenan Chen and Jason Chan published a groundbreaking study in the top journal Management Science, titled "Large Language Models in Creative Work: The Role of Collaborat

In fields such as social sciences, economics, and behavioral sciences, qualitative interviews have long been a crucial means of obtaining in-depth insights. However, traditional interview methods face challenges such as high resource consumption, significant time costs, geographical constraints, and biases introduced by interviewers’ subjectivity. In recent years, with the rapid advancement of artificial intelligence, particularly the rise of large language models (LLMs), AI-