Deep Learning Algorithms for Personalized Educational Content Delivery

Abstract

Modern deep learning advances enable personal educational content delivery through systems that shape learning experiences according to students' individual requirements. This research looks into the usage of Hugging Face Transformers in Transformer-Based Models for Intelligent Content Recommendation systems to enhance personalization quality of learning materials. The system uses self-attention and contextual embeddings to continuously evaluate student queries combined with learning activities and engagement behaviours for delivering immediate content suggestions. The incorporation of BERT and GPT pre-trained language models provides context-aware adaptation capabilities toward AI-driven tutor systems that create specific quizzes and tailor explanations and reading resources. Experimental data shows that the system enhances material alignment with student interests alongside better student interaction alongside more efficient adaptive feedback generation. Through its framework the recommendation system displays intelligent scalability combined with effectiveness while improving learning results and decreasing student cognitive burden. This investigation enhances the field of AI-driven education by creating learning systems that base their education on student-specific data.