Innovative Negative Sampling and Natural Language Processing Approach for Optimizing Knowledge Point Recommendations in Personalized Learning

This study explores an innovative approach to enhancing personalized learning through a negative sampling-optimized pedagogical recommendation model. The proposed methodology integrates artificial intelligence (AI), data mining, and natural language processing (NLP) techniques to improve the precision and personalization of knowledge point recommendations. Unlike conventional recommender systems that rely on positive feedback and generic algorithms, this approach incorporates negative sampling to identify and address knowledge gaps more effectively. The model enhances the learning experience by tailoring content delivery to individual student needs and optimizing recommendation accuracy and relevance. Furthermore, the study discusses the application of NLP for content analysis, user profiling, and explanation generation, ensuring deeper semantic understanding and improved recommendation transparency. Experimental validation on public educational datasets demonstrates the efficacy of this model in reducing training costs and advancing learning outcomes. This research contributes to the growing field of AI-driven education, offering a scalable and adaptive solution to personalized teaching and knowledge point delivery.