Exploration of AI-Enabled Smart Teaching in University English Education

Abstract

With the profound integration of artificial intelligence (AI) technologies into educational contexts, higher education in English is experiencing a transformative shift towards intelligent systems. This study systematically investigates the AI-driven reconstruction of pedagogical systems: technologically, speech recognition, natural language processing (NLP), and machine learning algorithms facilitate personalized
learning environments and multimodal environments (e.g., a 23% improvement in oral fluency via Beijing 101 Middle Schools AI pronunciation correction system, and a 15-fold efficiency boost in error detection through Nanjing Universitys Transformer-based writing evaluation
tool). Theoretically, constructivism and multimodal learning theories provide the foundation for dynamic knowledge graphs and immersive
scenarios (e.g., The implementation of Zhejiang Universitys XGBoost algorithm has enhanced knowledge retention by 31%, whereas VR
applications have increased linguistic complexity by 27%). Empirical results demonstrate significant effectiveness (Students at Shanghai Jiaotong University recorded an average writing score increase of 19.3%, while students in Tsinghua University attained an impressive accuracy
rate of 92% in specialized terminology translation). Nevertheless, challenges such as algorithmic bias and disparities in digital literacy continue to pose obstacles. The study proposes a tripartite ecosystem (hardware-platform-standards) for infrastructure development (e.g., opensource educational large language models) and a dual-track teacher training system (reducing veteran educators adaptation cycles by 60%
in Shanghais Jingan District), emphasizing a humanistic paradigm (e.g., Hangzhous AI Whitelist capping technological intervention at
30%).