Architecture Design and Implementation of AI-Native Learning Platform

Unlike traditional learning platform with the core functions of resource organization, teaching interaction, and learning process support, as well as AI-augmented learning platform that merely introduce AI plugins into partial functions, AI-native learning platform emphasize the deep integration of artificial intelligence technologies into platform architecture, business workflows, and user interactions. Facing the in-depth demands that the development of personalized education places on learning platform, this paper first proposes the concept of an “AI-native learning platform” and designs a three-layer architecture consisting of the bottom layer, middle layer, and top layer based on the fundamental reshaping of the development paradigm. With large language model serving as the cognitive hub, this architecture can enable dynamic organization and intelligent scheduling of system services, providing a new idea for the intelligent, personalized and sustainable evolution of learning platform. Then, taking University G in North China as an example, this paper elaborates on the transformation pathway for existing platforms evolve toward AI-native one, including the reconstruction of the underlying data system, the middle-layer middle platform, and top-layer interaction, as well as newly-added modules for AI-native learning platform. The engineering feasibility of the architecture is validated through the construction of the eLLM training platform. Finally, this paper discusses the problems encountered during the deployment and application of AI-native learning platform and puts forwards targeted directions for future research. The architecture of AI-native learning platform proposed in this paper provides a systematic design idea and implementation pathway for the construction of smart educational infrastructure, and can serve as a reference for the application of AI agent in large-scale educational scenarios.