Acceleration and Recursion in AI-Mediated Qualitative Research: Discussing Adapted Criteria for Qualitative Inquiry in the Techno-Epistemic Age

This article examines how artificial intelligence (AI) reshapes qualitative research (QR) by accelerating the circulation of classifications and introducing recursive feedback loops that reuse their own outputs. Drawing on Hacking’s concept of looping effects, we extend it through two mechanisms: acceleration (compressed loop time) and recursion (output re-entry). Reviewing current AI uses in QR, data synthesis, and retrieval, we identify four epistemic risks: over-smoothing, self-validation, performativity amplification, and externalized reflexivity. We then propose four provisional, reportable evaluative criteria suited for AI-mediated QR. These criteria translate long-standing commitments to transparency and credibility into the techno-epistemic age.