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December 6, 2025Consumer Psychology Review13 citations

From algorithm aversion to AI dependence: Deskilling, upskilling, and emerging addictions in the GenAI age

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TKTaeWoo Kim

Key Points

  • Deskilling in users can occur as GenAI tools are increasingly adopted, affecting their skill acquisition.
  • The framework identifies automation as a critical factor that influences both cognitive labor and user behaviors.
  • Analysis highlights the potential for behavioral addiction arising from increased dependence on AI systems.
  • Theoretical mechanisms suggest individual differences will determine whether GenAI leads to enhancements or erosion of capabilities.

Abstract

Abstract This conceptual paper explores the psychological consequences of consumer adoption of GenAI tools on consumer capability development. We propose a framework based on two orthogonal dimensions—Division of Cognitive Labor and Metacognitive Oversight—that yields four distinct patterns of human–AI interaction: Skilled Augmentation, Managed Automation, Unguided Effort, and Cognitive Surrender. Through synthesis of the literature on automation, cognitive offloading, and skill acquisition, we demonstrate how the shift from algorithm aversion to AI appreciation creates predictable trajectories in human capability. While some users maintain active oversight and achieve enhanced capabilities as AI‐augmented polymaths, our framework predicts a natural drift toward Cognitive Surrender, where users delegate both cognitive execution and metacognitive control to AI systems. We trace how this drift, accelerated by cognitive miserliness, effort aversion, and instant gratification dynamics, can progress from rational efficiency‐seeking through dependency to behavioral addiction to AI. The paper identifies theoretical mechanisms, boundary conditions, and individual differences that determine whether GenAI use leads to capability enhancement or erosion, proposing testable hypotheses for future empirical investigation.

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Cite This Study

TaeWoo Kim (2025) studied this question.

synapsesocial.com/papers/69337d09b3f947a0a125ac13https://doi.org/10.1002/arcp.70008
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