Dynamic Construction and Intelligent Recommendation of English Teaching Content via Knowledge Graphs to Enhance Personalized Learning Impact

Traditional English teaching methods often rely on static textbooks and uniform lesson plans, which cannot address the diverse needs of learners or adapt to their real-time progress. This lack of personalization limits student engagement and reduces overall learning effectiveness. Therefore, this study develops an innovative system that integrates knowledge graph technology with intelligent recommendation mechanisms to dynamically construct English teaching content. The novelty lies in enabling students to access scaffolded reference materials, aligning recommendations with their current knowledge state, and significantly increasing learning impact. The study is implemented across three educational scenarios: classroom teaching, online learning platforms, and teacher training programs. A graph neural network is used to model learner–content relationships, and a recommendation module combines collaborative filtering and content-based methods. System performance is evaluated using precision, recall, and F1 scores, alongside pre-post assessments of students’ learning outcomes and engagement levels. The results show that the system dynamically updates teaching content based on students’ knowledge mastery and learning progress, providing recommendations that achieve an accuracy rate above 85%. Learners’ overall performance improved by approximately 18%, with some cohorts reaching a 25% improvement. The proposed approach highlights how knowledge graph-driven content construction and personalized recommendation can effectively meet individualized learning needs, enhance student outcomes, and provide valuable insights for developing intelligent education systems.