摘要
The pedagogical promise of Competency-Based Education(CBE)has been historically undermined by pro-found challenges of scalability,creating an implementation gap between its theoretical merits and practicalapplication.This paper proposes a testable mechanism model wherein Artificial Intelligence(Al)enables the scaling of CBE through three interconnected pathways-diagnostic tracking,adaptive supply,and teacher or-chestration-formalized within a distributed cognition framework.To operationalize this model,this paper in-troduces 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 per-sonalized learning;second,it advances a set of falsifiable propositions to guide future empirical research;third,it formalizes the human-Al pedagogical relationship with operational design principles;and fourth,it presents an integrated governance and interoperability protocol for the responsible and effective implementation of Al in competency-based systems.
