Artificial neural networks often rely on fixed architectures and uniform training strategies, overlooking adaptive mechanisms found in biological learning. This work presents a conceptual framework for a biologically inspired training algorithm that mimics human skill acquisition through a progressive, difficulty-driven curriculum. Grounded in principles such as synaptic plasticity, dendritic computation, and modular learning, the proposed method restructures training into stages of increasing complexity, expanding the model’s architecture over time. Unlike other biologically inspired approaches that redesign neuron structures, this strategy retains standard neural components, allowing its application to conventional neural architectures while introducing an adaptive training schedule aligned with neurodevelopment. The model incrementally grows to match input difficulty, transferring and perturbing learned weights across stages to promote generalization and efficiency. Although not yet empirically validated, this work outlines a plan for systematic evaluation. Future phases will include the implementation of the training methods and generation of staged models. The framework offers a scalable and interpretable path toward energy-efficient learning and contributes to bridging biological and artificial intelligence.