AI-Driven Real-Time Feedback System for Technical Competency Enhancement in Edge-Cloud Environments

To addresses the challenge of low-latency, highaccuracy competency assessment in dynamic computational environments, an AI-Dirven real-time feedback system was constructed. This research focuses on three technical dimensions: distributed system architecture, hybrid machine learning models, and adaptive feedback mechanisms. By integrating learning behavior analytics, knowledge mastery assessment, and skill achievement tracking, the system implements stream processing for dynamic monitoring and personalized intervention. Experimental results demonstrate superior technical performance: 92% accuracy in real-time behavior prediction, 500 ms latency under 5,000 concurrent users, and 88% precision in skill assessment warnings. The system architecture leverages Apache Flink for stream processing and TensorFlow for distributed model training, providing a scalable framework for intelligent educational systems.