Active Inference: An Encompassing Theory of Learning

Educational research is currently characterized by a disjointed landscape, primarily dominated by the long-standing debate between Cognitive Load Theory (CLT) and constructivism. This paper evaluates whether Predictive Processing (PP) and Active Inference (AI) can provide a theoretically encompassing paradigm to unify these conflicting models. Unlike the passive information-processing model used in CLT, PP posits that learning is the active refinement of internal generative models driven by the minimization of prediction errors. The paper leverages the mechanistic framework of AI to explain diverse educational phenomena, including the role of prior knowledge, attention, desirable difficulties, and feedback. It further integrates socialemotional factors by linking the rate of error minimization to emotional valence and student motivation. While Active Inference offers superior neurobiological grounding and computational precision, the paper concludes that its practical utility in the classroom depends on whether its increased granularity translates into more effective instructional decisions than existing, simpler psychological frameworks.