Interpretive Variability in Generative AI Feedback: A Structural-Determinist Account of User Experience

User experience (UX) research on AI feedback typically treats response variability as noise to be minimized rather than as a phenomenon requiring explanation. Yet this variability is persistent—ranging from integration to dismissal—even when feedback procedure is held constant. This study examines this divergence through a controlled interaction in which 20 expert participants (doctoral/master's professionals) received AI-generated critiques of their own texts, produced under the same feedback methodology. Treating AI output as linguistic perturbation generated under a standardized procedure, we conducted a four-stage qualitative analysis tracing initial orientation, feedback features, reported stance and interactional trajectories. We identified three recurrent trajectories: congruent uptake (integration), pragmatic filtering (instrumental use) and defensive conservation (dismissal). These trajectories did not align with discipline or gender but reflected human–AI comparisons regulating receptivity. Drawing on Maturana's structural determinism, we reframe variability not as design failure but as a lawful outcome of history-shaped organization, shifting design goals from persuasion towards perturbation calibration and interpretive autonomy.