Designing Human-AI Orchestrated Classrooms: Mechanisms, Protocols, and Governance for Competency-Based Education

Abstract

The pedagogical promise of Competency-Based Education (CBE) has been historically undermined by profound challenges of scalability, creating an implementation gap between its theoretical merits and practical application. This paper proposes a testable mechanism model wherein Artificial Intelligence (AI) enables the scaling of CBE through three interconnected pathways—diagnostic tracking, adaptive supply, and teacher orchestration—formalized within a distributed cognition framework. To operationalize this model, this paper introduces novel constructs including the "Adaptive-Autonomy Curve" for systematically cultivating self-regulated learning in personalized environments, and a "Situated Performance-Based Assessment Pipeline" for authentic, scalable evaluation of complex skills. The primary contributions of this work are fourfold: first, it provides a rigorous conceptual taxonomy that delineates CBE from adjacent paradigms such as mastery learning and personalized learning; second, it advances a set of falsifiable propositions to guide future empirical research; third, it formalizes the human-AI pedagogical relationship with operational design principles; and fourth, it presents an integrated governance and interoperability protocol for the responsible and effective implementation of AI in competency-based systems.