Design and Application of English Smart Teaching System based on B/S Structure

The rapid advancement of technology in education has led to the development of intelligent teaching systems, particularly for language learning. This paper presents the design and implementation of an English Smart Teaching System based on Browser/Server (B/S) architecture, integrating adaptive learning and AI-driven feedback to enhance language acquisition. Traditional teaching methods often struggle with personalization, real-time feedback, and scalability, leaving gaps in addressing individual student needs efficiently. To overcome these challenges, the propose a hybrid AI engine combining BERT for natural language processing and LSTM networks for adaptive learning, creating a responsive and personalized learning environment. The proposed system achieves 97.4% accuracy in error detection, 97.9% recall in performance assessment, and a 95.2% adaptivity score, significantly outperforming existing models like fine-tuned BERT (94.1% accuracy) and LSTM+Attention (92.5% accuracy). Evaluation on 15,000 annotated student responses demonstrates its effectiveness in grammar correction, essay evaluation, and dynamic content adaptation, making it a robust solution for modern English language education.