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arxiv: 2605.00922 · v1 · submitted 2026-04-30 · 💻 cs.SE · cs.AI

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

Katja Karhu , Kari Smolander , Jussi Kasurinen
Authors on Pith no claims yet

Pith reviewed 2026-05-09 20:28 UTC · model grok-4.3

classification 💻 cs.SE cs.AI
keywords generative AIqualitative researchsoftware engineeringresearch philosophysmall-qBig Qethics in research
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The pith

Generative AI suits qualitative research mainly when the approach is positivist small-q rather than non-positivist Big Q.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper reviews the debate on whether generative AI belongs in qualitative research and spells out what this means for software engineering researchers. A sympathetic reader would see that the small-q positivist approach versus the Big Q non-positivist approach forms the main dividing line for AI acceptance. Skills in using the tools, ethical issues around data and bias, and personal researcher preferences add further layers to the choice. Understanding these factors helps researchers maintain rigor when AI offers speed in coding and analysis but risks undermining interpretive work.

Core claim

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.

What carries the argument

The small-q versus Big Q classification of qualitative research approaches, which ties AI compatibility to the underlying research philosophy.

If this is right

  • Software engineering researchers using small-q approaches can more readily incorporate generative AI for tasks like data coding or summarization.
  • Big Q researchers should exercise greater caution or avoid AI to maintain interpretive depth and researcher involvement.
  • Ethical considerations around data privacy and AI bias must be addressed regardless of approach.
  • Developing AI literacy becomes essential for researchers deciding on tool use.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • This framework might encourage software engineering teams to declare their qualitative stance upfront when publishing AI-assisted studies.
  • Neighbouring fields like information systems research could adopt similar distinctions to guide AI tool policies.
  • A testable extension would be to compare AI-assisted and fully manual qualitative analyses in SE papers for differences in findings depth.

Load-bearing premise

Existing literature provides a neutral and complete summary of views on generative AI in qualitative research, and that philosophy, skills, ethics, and preferences are the primary factors researchers weigh.

What would settle it

A survey of software engineering researchers finding no consistent difference in generative AI adoption between small-q and Big Q users would challenge the central criterion.

Figures

Figures reproduced from arXiv: 2605.00922 by Jussi Kasurinen, Kari Smolander, Katja Karhu.

Figure 1
Figure 1. Figure 1: AI as a parallel researcher computer science or formal methods background, qualitative research is often viewed through a small-q lens. This is because of the strong quantitative tra￾dition in software engineering [17, 30]. Therefore, although the small-q and Big-Q division of qualitative research did not originate in the software engineer￾ing context, it describes the ”opposing forces” at play in qualitat… view at source ↗
Figure 2
Figure 2. Figure 2: AI as a layer between researcher and subject [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

0 major / 2 minor

Summary. The paper summarizes the ongoing debate among qualitative researchers on the suitability of generative AI for qualitative research and discusses its implications specifically for software engineering researchers. It identifies the distinction between small-q (positivist or post-positivist) and Big Q (non-positivist) qualitative approaches as a primary criterion for deciding whether generative AI can be appropriately used. The paper further states that researchers' skills, ethical considerations, and personal preferences also factor into such decisions.

Significance. If the literature synthesis is accurate and balanced, the paper could provide a helpful entry point for software engineering researchers navigating the integration of generative AI into qualitative work by distilling key philosophical and practical considerations from the broader methods literature. Its value lies in contextualizing an active debate for a specific disciplinary audience rather than in presenting new empirical findings or formal derivations.

minor comments (2)
  1. The abstract and title use informal phrasing (e.g., 'To Vibe Research or Not to Vibe Research?') that may benefit from a more conventional academic tone or explicit definition of 'vibe research' to ensure accessibility for readers unfamiliar with the term.
  2. Consider adding a brief table or structured list in the main text that maps the small-q/Big Q distinction to specific generative AI use cases (e.g., coding, theme generation) to make the central criterion more concrete and actionable for software engineering practitioners.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for their review and for recommending minor revision. The referee's summary accurately captures the manuscript's contribution as a synthesis of the ongoing debate on generative AI in qualitative research, with specific implications for software engineering researchers. We appreciate the recognition of its potential value as an entry point for the disciplinary audience.

Circularity Check

0 steps flagged

No significant circularity; literature summary is self-contained

full rationale

The paper presents a summary of ongoing external debate on generative AI in qualitative research and its implications for software engineering. It advances no equations, derivations, fitted parameters, hypotheses, or novel empirical results. The central claim—that small-q vs. Big Q approaches plus skills/ethics/preferences influence AI use—is explicitly framed as a distillation of existing literature rather than a constructed output. No self-citation chains, ansatzes, or renamings appear in the provided text, and the work does not reduce any result to its own inputs by definition. This is the expected finding for a non-derivational discussion paper.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the standard distinction between small-q and Big Q qualitative research paradigms drawn from prior literature in the field; no new free parameters, axioms invented for this paper, or postulated entities are introduced.

axioms (1)
  • domain assumption The distinction between small-q (positivist or post-positivist) and Big Q (non-positivist) qualitative research approaches is a valid and major criterion for evaluating AI use.
    Invoked directly in the abstract as one of the primary determinants.

pith-pipeline@v0.9.0 · 5396 in / 1283 out tokens · 39208 ms · 2026-05-09T20:28:05.672113+00:00 · methodology

discussion (0)

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