AI-Driven Content Recommendation in Learning Management Systems (LMS): A Hybrid Filtering Approach

The incorporation of technology in teaching has drastically changed how learners engage with scholarly resources for a particular subject. Today's Learning Management Systems contain innumerable resources that require intelligent recommendation systems to optimize learner interaction and achievement. This study focuses on creating an AI-based content recommendation system using hybrid filtering, where the recommendation is based on both content and user activity within the LMS. The main focus of the system is to mitigate the cold-start problem and over-specialization of traditional recommender systems. A prototype was built with a custom LMS that incorporated a hybrid recommend system which was tested in an experimental setting with students from a local university against traditional filtering techniques. Data regarding precision, recall, user engagement, and satisfaction was collected and analyzed. Results from the study showed the integrated use of diverse AI techniques more accurately met the user's needs regarding educational content access as learners rated their experience more positively. The research demonstrates the significant advantages that fusion models present in the modern educational context while outlining the direction of research that adaptative learning systems require.