World Models in Artificial Intelligence: Sensing, Learning, and Reasoning Like a Child
Current AI world models are constrained by pattern-recognition paradigms, limiting predictive accuracy, environmental reasoning, and decision interpretability—hindering genuine understanding. To address the fundamental deficiency in child-like structured and adaptive world modeling, this paper systematically integrates Piaget’s theory of cognitive development for the first time, establishing a dynamically evolving world model framework. Methodologically, it unifies six interdisciplinary pillars: physics-informed modeling, neuro-symbolic learning, continual learning, causal inference, human-AI collaboration, and responsible AI—thereby realizing a hybrid architecture that synergizes statistical learning with cognitive mechanisms. Our core contribution is the proposal of a “cognition-driven world model” paradigm, which substantially enhances model interpretability, adaptability, and embodied reasoning capability. This work lays a theoretical foundation and provides a concrete technical pathway toward next-generation AI that is comprehensible, trustworthy, and cognitively grounded.