Cognitive Atrophy Paradox of AI–Human Interaction: From Cognitive Growth and Atrophy to Balance
Citation. Kabashkin, I. (2025). Cognitive atrophy paradox of AI–human interaction: From cognitive growth and atrophy to balance. Information, 16(11), 1009. https://doi.org/10.3390/info16111009
Summary. This peer-reviewed article proposes a cognitive co-evolution framework in which reflective AI use can support metacognitive growth while over-delegation and uncritical reliance can contribute to cognitive atrophy. It introduces a Cognitive Sustainability Index (CSI) composed of autonomy, reflection, creativity, delegation, and reliance, then uses illustrative simulations and professional examples to describe cognitive-growth, balance, and atrophy zones. The paper also proposes applied cognitive management: monitoring and intervention across individual, educational, professional, and institutional settings.
Key claims
- The paper frames human–AI interaction as a dynamic balance: reflective engagement, feedback, and creative co-thinking can support cognitive growth, whereas excessive delegation, automation complacency, and uncritical reliance can displace analytical effort and epistemic responsibility.
- Its proposed CSI combines human-side autonomy, reflection, and creativity with delegation to and reliance on AI; the paper treats the resulting ratio as a context-sensitive indicator of cognitive sustainability rather than a fixed personal trait.
- In illustrative, nonparticipant simulations, a high-autonomy/high-reflection researcher profile occupies a growth zone while a higher-delegation/higher-reliance student profile occupies an atrophy zone; these examples demonstrate the index’s intended interpretability, not empirical validation.
- The applied cognitive management framework recommends reflective prompts, explainable outputs, intentional slow-downs, feedback, and adaptive task design to preserve human reasoning, creativity, and accountability while retaining AI support.
Evidence & limitations
- Peer-reviewed open-access conceptual and simulation-based article in Information; it synthesizes prior literature and introduces frameworks rather than reporting a controlled field study or validated measurement development.
- CSI uses five 0–10 behavioral parameters and context-specific weights, but the study’s researcher and student profiles are illustrative; it does not establish reliability, construct validity, predictive validity, thresholds, or causal effects in real workers.
- Broad claims span education, professional work, software development, health care, and policy, so workplace generalization and intervention effectiveness require longitudinal, domain-specific, and independently replicated evidence.