To Vibe Research or Not to Vibe Research? Generative AI in Qualitative Research

2026-04-30

AI Summary

This study examines the applicability and contested boundaries of generative AI in qualitative research. It innovatively distinguishes between “small-q” positivist and “big-Q” non-positivist paradigms, using this dichotomy as a central criterion to develop a multidimensional decision framework for AI adoption that incorporates researcher expertise, ethical considerations, and personal preferences. Through a comprehensive literature review and theoretical analysis, the paper clarifies the conditions under which generative AI is methodologically justifiable or constrained within each paradigm. The findings offer valuable methodological guidance and practical insights for conducting qualitative research—particularly in fields such as software engineering—where the integration of AI tools raises both opportunities and epistemological challenges.

Abstract

There has been intense debate among qualitative researchers about whether generative AI is suitable for qualitative research. In this paper, we summarize the broader ongoing discussion of generative AI in qualitative research and its implications for software engineering researchers. The qualitative research approach, small-q (positivist or post-positivist) or Big Q (non-positivist), is among the major criteria for determining whether generative AI can be used in qualitative research. In addition to research philosophy and research approach, skills, ethics, and personal preferences also play a role in researchers' decisions about whether to use AI in qualitative research.

Problem

• generative AI
• qualitative research
• research philosophy
• ethics
• software engineering

Innovation

• Generative AI
• Qualitative Research
• Research Philosophy
• Methodological Approach
• Research Ethics