Cognitive adaptation in the intelligence era (CAIE): a dynamical systems framework for education reform in the age of artificial intelligence

This paper introduces the Cognitive Adaptation in the Intelligence Era (CAIE) model, a dynamical systems framework that formalises how major technological revolutions impose cognitive adaptation pressure on human populations, and what this implies for the structural reform of education. We hypothesise that technology-driven cognitive pressure can be represented as a time-varying forcing function in an ordinary differential equation (ODE) system, and that education design—when informed by motivational neuroscience—can sustain positive cognitive adaptation even under accelerating AI pressure. The theoretical model defines a seven-dimensional cognitive-affective ability vector S ( t ) ∈ R 7 encompassing working memory, task-switching efficiency, indexical memory, multi-agent management, metacognition, emotional intelligence, and identity stability. The evolution of S ( t ) is governed by an ODE integrating four forces: (1) an effective education function modulated by dopamine reward prediction error (RPE), (2) Sigmoid-shaped technology pressure calibrated against three historical revolutions (steam engine, internet, AI), (3) a three-component cognitive resistance term grounded in neuroplasticity, information-theoretic bandwidth constraints, and habitual inertia, and (4) cumulative cognitive burden from prior technological epochs. Theoretical simulation using Runge–Kutta methods yields four hypotheses: (a) traditional curricula become insufficient after the AI inflection point ( ∼ 2023); (b) CAIE-designed education with RPE-driven motivation maintains positive adaptation across all seven dimensions; (c) the optimal number of concurrently managed AI agents peaks at n ∗ ≈ 3 for untrained individuals (a prediction consistent with BCG 2026 empirical data, n = 1 , 488 ) and extends to 5–8 with deliberate training; (d) a critical education delay threshold τ ∗ ≈ 1.25 years exists, beyond which remedial intervention becomes significantly less effective. We emphasise that these numerical values ( n ∗ ≈ 3 , τ ∗ ≈ 1.25 years) are illustrative outputs of a theoretically parameterised model rather than empirically established constants; they are presented as quantitative hypotheses to be tested, not as calibrated measurements. These hypotheses are theoretically aligned with evidence from 20+ peer-reviewed studies spanning ADHD epidemiology, cognitive load theory, dopamine-driven learning, and working memory research. Four prospective experiments are proposed to empirically test the model’s predictions. CAIE addresses a gap in the digital education literature by providing the first mathematically formalised, computationally solvable framework that simultaneously models technology-driven cognitive pressure, curriculum design, and motivational dynamics.