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The Cognitive Cost of AI Assistance: Protecting Human Thinking in the Age of Generative AI
Citation. Westover, J. H. (2025). The Cognitive Cost of AI Assistance: Protecting Human Thinking in the Age of Generative AI. Human Capital Leadership Review, 26(1). https://doi.org/10.70175/hclreview.2020.26.1.6
Summary. Westover presents a practitioner-oriented synthesis arguing that generative-AI assistance can reduce cognitive engagement in knowledge work while improving short-term efficiency. Drawing on prior cognitive-offloading, automation-complacency, expertise, and human–automation research, the article proposes organizational safeguards including explicit task boundaries, unassisted thinking periods, and critical engagement with AI outputs. It does not report a new empirical study of generative-AI use.
Key claims
- Generative AI can offload higher-order activities such as language formulation, idea generation, and aspects of critical thinking, extending familiar cognitive-offloading concerns beyond memory and calculation.
- The article argues that uncritical AI use may weaken knowledge retention, expertise development, situation awareness, and creative capacity over time, especially where routine cognitive effort is displaced.
- It proposes human-primary boundaries for strategic, novel, and high-stakes decisions; alternating assisted and unassisted work; and practices that require users to evaluate and modify AI outputs.
- It recommends metacognitive training, reflection routines, and measures that track thinking quality as well as productivity when organizations introduce AI assistance.
Evidence & limitations
- This is a practitioner article that synthesizes established cognitive-science and human–automation literature rather than reporting original data on generative-AI users.
- Its claims about longer-term effects of generative-AI assistance are framed as potential consequences and organizational design implications; causal effects, boundary conditions, and effectiveness of the proposed safeguards require direct empirical testing.