Today, educational systems are not personalized enough or adaptive enough to provide effective support for learning, leading to low engagement, inefficient learning, and lack of access to relevant learning materials for students. To resolve these issues, a new approach called AI-Integrated Adaptive Educational Web Platform Using Hybrid Recommendation Techniques is presented to improve personalized education with the aid of Artificial Intelligence techniques. The platform's proposed recommendation system utilizes a hybrid technique which integrates both content-based and collaborative filtering algorithms to understand users' preferences and learning patterns, and then generate custom course and material recommendations. Moreover, Natural Language Processing is also supported to facilitate the understanding of queries and provide chatbot interaction to support students' real-time learning assistance. The proposed system is an adaptive system, adapting dynamically to individual learners, unlike traditional e-learning systems that are based on a static content delivery. The experimental evaluation confirms that the proposed platform is successful in improving the accuracy of the recommendations and in improving the interaction of users, thus demonstrating its effectiveness in an intelligent education environment in the modern era.

adaptive learning;hybrid recommendation techniques;content-based filtering;collaborative filtering;natural language processing;personalized education