Can machines perform a qualitative data analysis? Reading the debate with Alan Turing

Stefano De Paoli
12/2/2025
cs.CYcs.AIcs.CL

No confident code implementation found for this paper. 1 possible match can be shown below.

Abstract

This paper reflects on the literature that rejects the use of Large Language Models (LLMs) in qualitative data analysis. It illustrates through empirical evidence as well as critical reflections why the current critical debate is focusing on the wrong problems. The paper proposes that the focus of researching the use of the LLMs for qualitative analysis is not the method per se, but rather the empirical investigation of an artificial system performing an analysis. The paper builds on the seminal work of Alan Turing and reads the current debate using key ideas from Turing "Computing Machinery and Intelligence". This paper therefore reframes the debate on qualitative analysis with LLMs and states that rather than asking whether machines can perform qualitative analysis in principle, we should ask whether with LLMs we can produce analyses that are sufficiently comparable to human analysts. In the final part the contrary views to performing qualitative analysis with LLMs are analysed using the same writing and rhetorical style that Turing used in his seminal work, to discuss the contrary views to the main question.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. 1 weaker match is hidden by default — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Cite this paper

@article{paoli2025machines,
  title  = {Can machines perform a qualitative data analysis? Reading the debate with Alan Turing},
  author = {Stefano De Paoli},
  year   = {2025},
  eprint = {2512.04121},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2512.04121v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2512.04121.svg)](https://codeofpaper.com/paper/2512.04121)

Discussion