Optimization of Personalized English Learning Paths through Mobile Interaction Technology

With the development of information technology (IT), particularly the widespread application of mobile internet and smart devices, traditional methods of English language learning can no longer meet the personalized needs of modern learners. The design and recommendation of personalized learning paths have become key issues in enhancing learning outcomes. Current study primarily focuses on personalized recommendation systems based on big data and artificial intelligence (AI) algorithms. While these systems have achieved a certain degree of accuracy in recommending learners’ interests and learning content, problems such as recommendation precision, dynamic adaptation to changing interests, and insufficient integration of diversified learning scenarios persist. Therefore, improving the adaptability of personalized learning systems through more intelligent and dynamic learning path optimization methods remains a pressing challenge in this field. Building on existing research, a personalized English learning interest point recommendation model based on the graph convolutional network (GCN) was proposed, and personalized learning paths were optimized by incorporating multidimensional contextual information. The GCN was used to uncover the relationships between learners and knowledge points, thus constructing a precise interest point recommendation mechanism. Additionally, learning paths were dynamically adjusted by considering learners’ historical behaviors, learning progress, and situational context, offering a personalized learning experience. This study advances the development of personalized learning recommendation technologies and provides English learners with a more intelligent and precise learning path optimization solution.