Revolutionizing College English Education: An AI-Powered Tutoring and Learning Path Framework

In the digital era, the integration of technology into classrooms has transformed traditional learning environments. Among these advancements, Intelligent Tutoring Systems (ITS) stand out as one of the most promising innovations in educational technology, offering personalized and adaptive learning opportunities. This study focuses on developing a College English Intelligent Tutoring System with efficient learning path planning using Artificial Intelligence (AI). The research introduces a novel Intelligent Honeybee Mating Optimized Dynamic Long Short-Term Memory (IHMO-DLSTM) model to enhance recommendation accuracy and efficiency. Inspired by honeybee mating behaviors, the IHMO method effectively balances exploration and exploitation in optimization tasks. The DLSTM component analyzes students’ learning behaviors and generates personalized recommendations based on their unique learning paths. The system leverages data on student performance, behaviors, preferences and interactions to create a foundation for tailored learning pathways. Data preprocessing involves cleaning and normalization to ensure the quality and reliability of the dataset. The proposed IHMO-DLSTM technique addresses the challenge of low recommendation accuracy in personalized learning and significantly improves the efficiency of the recommendation process. Experimental results demonstrate that the IHMO-DLSTM model outperforms existing approaches, achieving a precision of 0.9122, a recall of 0.9345 and an F1-score of 0.9560. These metrics indicate more accurate, comprehensive, and customized learning path recommendations. The results also confirm that the proposed method substantially improves alignment between personalized learning paths and learner needs, delivering better outcomes compared to traditional algorithms. The findings highlight the potential of the proposed College English Intelligent Tutoring System to revolutionize personalized education by providing accurate and efficient learning path recommendations, enhancing both student engagement and educational outcomes.