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This study explores the methods and applications of constructing an intelligent education knowledge graph based on multi-modal learning materials (text, images, videos) Through deep learning algorithms for feature extraction and relationship modeling, a knowledge graph containing 120 entities and 100 relationships was built with an accuracy of 87 \%. The graph demonstrates excellent performance in personalized learning path recommendations, capable of recommending based on students’ prior knowledge and learning styles, leading to a 27 \% improvement in learning efficiency. The study also considers the “uncanny valley” phenomenon, avoiding discomfort caused by AI mimicking human behavior excessively, ensuring a positive user experience.
Li et al. (Fri,) studied this question.