Loop-Based Learning: Why Education Is Built on a Theory Neuroscience Abandoned, and a Design Framework Derived from Predictive Processing

This paper argues that education and corporate training are built on constructivist pedagogy — a theory of learning that neuroscience replaced with predictive processing over a decade ago. AI learning tools (Google LearnLM, ChatGPT Study Mode, Khanmigo) have widened this gap by scaling the wrong theory with unprecedented efficiency. The paper's central contribution is the "accumulation thesis": single prediction errors produce doubt, not revision, because high-precision priors resist single disconfirmations. Genuine learning requires accumulated prediction errors across multiple encounters where consequence persists between them. From this analysis, the paper derives Loop-Based Learning — a design framework specifying loop architecture, consequence persistence, spacing, and re-entry parameters calibrated to learner model precision.