Design and Application of an English Smart Teaching System based on Intelligent Large Language Model

Modern language education requires flexible, intelligent platforms that can adapt to diverse learning needs while maintaining accessibility. Web-based smart teaching systems present an ideal solution, offering device-independent access and centralized management of educational resources. However, current implementations often face limitations in personalization accuracy, real-time interaction quality, and adaptive content delivery, resulting in suboptimal learning experiences. To overcome these challenges, this study develops an intelligent system combining knowledge graph-enhanced recommendation Transformer-Based Adaptive Learning Model (TALM), algorithms with real-time performance analytics. The solution employs a hybrid approach integrating collaborative filtering (98.2% accuracy) and deep neural networks (97.6% prediction precision) to deliver personalized learning paths. System evaluation demonstrates outstanding metrics: content recommendation relevance (96.9%), response latency (98.4% requests processed <0.3s), knowledge retention improvement (95.8%), and user engagement rates (97.1%). The system successfully merges web technology's accessibility with advanced AI-driven personalization, creating a robust framework for English language education. Its consistently high-performance metrics confirm effectiveness in addressing critical gaps in digital language learning while maintaining the flexibility required for varied educational contexts. This approach establishes a new standard for intelligent, web-based teaching systems that dynamically respond to learner needs.