Fusion Algorithm of Large-scale Language Model and Knowledge Graph for English Intelligent Teaching

Abstract

This study presents a fusion algorithm that integrates large-scale language models and knowledge graphs to enhance English intelligent teaching. In response to the challenges of traditional language instruction and the opportunities afforded by advanced technologies, the fusion algorithm aims to personalize learning experiences, provide contextual understanding, and offer tailored feedback to learners. The methodology encompasses dataset selection, preprocessing, training language models, constructing the fusion algorithm, and designing an evaluation framework. Statistical results reveal the algorithm's effectiveness across various tasks, including content generation, semantic enrichment, personalized learning, and adaptive feedback. Key performance metrics such as BLEU score, cosine similarity, accuracy, precision, and recall demonstrate the algorithm's proficiency in generating contextually relevant content, enriching educational materials with semantic information, and adapting to individual learner needs. Comparison with existing approaches highlights the algorithm's superiority in enhancing the quality and effectiveness of English language instruction. Pedagogical implications underscore the potential of the fusion algorithm to create engaging and inclusive learning environments that cater to diverse learner needs. However, challenges such as bias in training data and algorithmic interpretability remain areas for future research. Overall, this study contributes to advancing the state-of-the-art in English intelligence teaching and lays the groundwork for further exploration of AI technologies in education.