Qualitative research

Plan a qualitative-coding pilot, protect participant context, review AI suggestions, and document your decisions with a reusable prompt and example.

Recording of the ACR webinar on vibe coding and AI-assisted interviewing for consumer researchers, held on July 7, 2026.

Chinese-language course on AI-assisted qualitative research. Find provider information, access requirements and a practical coding exercise.

Design an AI interview pilot with neutral probes, stop-handling tests, a data-flow review and a worked library-search example for researchers.

Consumer psychologists debate AI-assisted grounded theory, management scholars audit 353 computational-linguistics papers, and legal researchers argue that nobody is independently benchmarking the AI tools already in professional use.

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.

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

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

On October 15, New Scholars hosted a webinar featuring Stine Grodal and Henri Schildt on using AI for qualitative studies. Here is A Best Practice Guide to AI-Augmented Qualitative Research based on Stine and Henri's paper and their webinar. 1.0 Introduction: A New Frontier for Qualitative Inquiry New artificial intelligence (AI) tools are fundamentally reshaping the landscape of knowledge work, creating both significant opportunities and profound challenges for the qualitati

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

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

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-

Explore the original MimiTalk interface as a historical archive, with preserved screenshots and links to a current AI interview pilot workflow.

Evaluate an AI interview system with a clear data-flow map, synthetic tests, stop-handling checks and current provider documentation.