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What is gained and lost when students delegate cognitive labor to AI? Is this simply cognitive offloading, or something fundamentally different?

Anastasiia Sharkova, CPM' 28

· paper

Abstract

The essay examines whether handing over part of the cognitive workload to generative AI is merely an extension of the existing offloading practice or whether it represents a completely different phenomenon. Drawing on theoretical distinctions between rule-based automation and AI, essay analyses three mechanisms through which AI delegation may reduce thinking abilities: cognitive fatigue, automation-induced skill decay, and population-level homogenisation of reasoning. Empirical evidence from EEG studies, software development experiments, and educational research indicates significant negative correlations between AI reliance and cognitive performance, including reduced brain connectivity and lower conceptual understanding. However, outcomes depend on interaction type—AI that “asks” through Socratic questioning sustains skills, whereas AI that “tells” fosters dependency. Essay contests that for discontinuity from traditional offloading based on scale, homogenisation, and developmental asymmetry: AI substitutes higher-order cognition, reduces collective reasoning diversity, and prevents learners from acquiring competences. Thus, while AI enhances immediate task output, it generally undermines long-term cognitive competence.

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