Cognitive Firewall Collapse: Speed Bypass, Rational Delegation and Selection Pressure in Assisted-learning

Abstract

Background. Generative AI can raise task performance while leaving metacognitive calibration, unaided performance, or skill formation unchanged or impaired. Existing work names the accompanying drop in reflection "metacognitive laziness", a label that locates the problem in learner effort and directs intervention towards attitude. That framing leaves unexplained why capable learners stop reflecting in environments where reflecting has become the costlier route. Objectives. This article introduces speed bypass as a structural mechanism for the same observable reduction in metacognitive activity, and situates it within a prospective risk termed cognitive firewall collapse. Methods. The analysis is conceptual. It defines the cognitive firewall as the set of independent human processes that originate, test, reject, and govern AI-mediated cognition; specifies speed bypass as the case in which low response latency materially contributes to making the AI-first route locally preferable; and separates system-side latency from human elapsed time as measurement variables. Recent randomized and experimental studies of guardrailed tutoring, self-generation-first workflows, judgement-before-recommendation designs, and manipulated latency supply the observed precursors. Results and Conclusions. The published evidence supports a precursor proposition: workflow design changes what remains available on the human side after assistance, while durable de-skilling and a generational loss of independent assessors remain unestablished. The educational question therefore shifts from how to make learners try harder towards which capacities must be formed before delegation and who holds authority to release developmental friction. Slow AI is proposed as a temporary developmental regime, and three falsifiable test programmes are specified.