EdgeKG ‐ EN : A Dynamic English Knowledge Graph Framework With Edge Computing‐Driven Optimization

ABSTRACT Addressing the limitations of traditional cloud architectures in timeliness, heterogeneous adaptability, and energy efficiency, this paper presents EdgeKG‐EN, an edge‐intelligence‐driven dynamic knowledge graph framework for adaptive English education. The framework establishes three core mechanisms: temporal attention‐based dynamic graph modeling for real‐time concept evolution tracking, lightweight knowledge distillation protocols that enable efficient edge‐device updates, and reinforcement learning‐based scheduling strategies that optimize resource allocation. Multimodal learning alignment ensures cognitive‐semantic consistency while privacy‐preserving mechanisms guarantee data security. Experiments demonstrate that the framework significantly enhances knowledge reasoning timeliness and personalized recommendation accuracy under low‐power operation, providing a novel solution for distributed educational scenarios.