2025 marked a pivotal turning point of explosive growth and deep integration of artificial intelligence (AI) technology in the education sector. The role of AI in education is undergoing a systematic shift from traditional "standardized teaching assistance" to "personalized human-machine collaborative enhancement." This article examines the core advancements in AI-driven education during the year, focusing on four key areas: generative AI (GenAI), intelligent teaching agents, edge AI educational hardware, and educational AI governance frameworks. Research indicates that the global educational AI ecosystem demonstrates a trend of balancing technology-driven innovation with educational equity. The study concludes that the current priority has shifted toward the compliant deployment and engineering implementation of AI technologies in educational settings. Future educational paradigms will emphasize the synergy of "AI + Human Intelligence (AI+HI)," propelling AI beyond a mere efficiency tool to evolve into a "human-machine collaborative augmented educational partner" that promotes students' holistic development and enables truly personalized, adaptive teaching.

With the deep integration and large-scale application of Internet of Things (iot), 5G communication and artificial intelligence technologies, global data is experiencing explosive growth. The limitations of traditional centralized cloud computing architectures in terms of ultra-low latency service demands, massive bandwidth consumption and data privacy protection are becoming increasingly prominent. Edge computing, as an important extension of distributed systems, effectively compensates for the shortcomings of cloud computing in real-time response and bandwidth optimization by sinking computing, storage and application capabilities to the network edge. However, the inherent characteristics of edge nodes, such as limited computing power and heterogeneous resources, make it difficult for them to independently undertake large-scale global optimization tasks. Therefore, "cloud-edge collaboration" has become the core paradigm to solve the contradiction of computing services in the Internet of Things era. From the perspective of distributed system theory, this paper systematically sorts out the evolution context of the cloud-edge collaborative architecture, and deeply analyzes the three-layer logical model of "endpoint device layer - relay edge layer - core cloud computing layer" and its internal collaborative mechanism. Focus on researching key technologies such as computing offloading decision optimization, data consistency maintenance, cloud-edge resource orchestration and communication, and construct multi-objective optimization models and efficient solutions; Conduct empirical analysis based on typical scenarios such as smart cities, Industry 4.0, and intelligent connected vehicles to verify the technical superiority of the cloud-edge collaborative architecture. Finally, the future research directions such as green edge computing, endogenous security mechanisms, and AI adaptive collaboration are prospected. The research results can provide references for the theoretical innovation and engineering implementation of cloud-edge collaborative systems.