Modeling and Analysis of College English from the Perspective of Connected Intelligent Algorithms

To explore the realm of English education through contemporary advancements in artificial intelligence (AI), language education methods have developed significantly traditional approaches that usually have trouble with individualized learning needs and cannot handle diverse proficiency levels. This paper reveals the most prominent problem of inefficiency and non-personalized learning experiences in college English as a significant concern in the existing literature. In response, it suggests a novel method called Artificial Intelligence based English Teaching Method (AI-ETM). It has linked intelligent algorithms that can dynamically adapt teaching strategies and learning materials according to individual student’s needs, rate of comprehension and proficiency levels. The main aim of AI-ETM is to integrate AI technologies with hybrid particle swarm optimization (PSO) into English language teaching to improve effectiveness and efficiency and provide personalized learning experiences for students. This paper expects several outcomes from the implementation of AI-ETM. Among these is enhanced student engagement and motivation due to personalized learning experiences customized according to personal preferences and level of competence. Additionally, AI-ETM is expected to improve learning outcomes by offering targeted feedback and adaptive learning resources. This paper suggests that once AI-ETM is introduced, efficiency in delivering language instruction services will improve, thereby optimizing the allocation of educational resources.