Research on Empowering College English Intelligent Teaching Mode with Artificial Intelligence, Constructing Personalized Learning Path and Efficiency Evaluation

Abstract

Under the dual drive of educational digital transformation and new liberal arts construction, college English teaching still faces inaccurate learning-state recognition, homogeneous content supply, weak supervision of learning processes, limited personalized guidance, and single evaluation methods. This study constructs an artificial-intelligence-enabled smart teaching model for college English by integrating big data analysis, natural language processing, knowledge graphs, adaptive learning, and intelligent evaluation. On the basis of learner profiling, precise diagnosis, path generation, smart teaching implementation, multidimensional evaluation, and iterative optimization, the model forms a closed loop of perception, intervention, feedback, and adjustment. A semester-long teaching experiment shows that the experimental group improved from a pre-test mean of 62.34 to a post-test mean of 78.56, substantially exceeding the control group’s gain. The framework supports differentiated listening, speaking, reading, writing, and crosscultural communication training while improving teaching efficiency. For engineering-oriented smart classrooms, its data-driven learning path design also provides a methodological reference for wireless communication-supported learning environments and electromagnetic-compatible educational sensing systems.