The Deviation and Guidance: A Dynamic Model of Learning from the Perspective of the Unified Dynamic Model of Mind (UDMM)

Abstract In a world where artificial intelligence is evolving rapidly, traditional education is in a crisis. The current system, by focusing on rote memorisation and the application of pre-defined skills, risks preparing learners for the world of yesterday, teaching them to be efficient cogs in a machine that is rapidly becoming automated. This paper introduces a novel theoretical framework based on the Unified Dynamic Model of Mind (UDMM) to address this existential challenge. Here, learning is understood as a continuous movement around a virtual attractor (W_{\text{ideal}}), which represents a comprehensive simulation of reality and embodied satisfaction. We propose that learning environments can either guide the internal model towards creative curiosity and cognitive independence, or deviate it into compulsive orbits driven by an external, coerced attractor (W_{\text{coerced}}). We demonstrate how Kullback-Leibler (KL) divergence and orbital dynamics can design supportive environments. Emotional aspects function as an error correction mechanism, while social aspects act as predictive scaffolds for coordination. This model offers a deeper understanding of learning differences, redefines success and motivation in the age of AI, and opens avenues for interventions targeting the cognitive attractor rather than overt behaviour. A supplementary appendix illustrates deviation mechanisms.