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Can Machines Perform a Qualitative Data Analysis? Reading the Debate With Alan Turing

Stefano De Paoli · Social Science Computer Review · 2026

This paper reflects on the literature that rejects the use of Large Language Models (LLMs) in qualitative data analysis (QDA). It illustrates through empirical evidence and critical reflections why the current debate is focusing on the wrong problems. The paper proposes that a key focus of researching the use of the LLMs for QDA is the empirical investigation of a hybrid/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’s “Computing Machinery and Intelligence.” This paper reframes the debate on QDA with LLMs and states that rather than asking whether machines can perform qualitative analysis in principle, we should ask whether with LLMs and researchers together can produce analyses that are sufficiently comparable to human analysts alone. In the final part the contrary views to performing QDA with LLMs are analysed using the same writing and rhetorical style that Turing used in his seminal work.

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