Personal Learning Intelligence (PLI): Learning as a Persistent, Context-Aware Multimodal System Enabling Reinforcement-Driven Adaptation and the Longitudinal Preservation of Learner Agency

This working paper introduces Personal Learning Intelligence (PLI) as a conceptual framework for rethinking learning systems in the age of artificial intelligence. The paper argues that contemporary learning platforms, despite advances in AI-driven assistance, remain structurally misaligned with well-established insights from the learning sciences, particularly regarding persistence, context, and adaptation over time. Drawing on research from cognitive science, educational theory, intelligent tutoring systems, and learning analytics, the paper reframes learning as a persistent, context-aware, multimodal system that adapts through reinforcement from learner behaviour while preserving learner agency longitudinally. It introduces the Three Waves of Learning as a constraint-based model for understanding the historical evolution of learning systems and explains why existing content-centric and session-based approaches systematically plateau. The paper is conceptual and epistemic in nature. It does not present new empirical findings or propose a specific technical implementation. Instead, it articulates a system-level abstraction, core properties, and a non-technical conceptual architecture intended to support cumulative progress across research, system design, and policy. This version (v1.0) is released as a public prior-art disclosure.