Comparative Analysis of Various Recommender Systems for Learners Using Artificial Intelligence

Recommender systems are becoming an essential component of personalized learning environments, providing individual learners with individualized instructional materials. These systems optimize material delivery, increase engagement, and improve academic achievements by leveraging Artificial Intelligence (AI) for learner-centric suggestions. This paper provides a comparative examination of several AI-driven recommender systems utilized in academia. Various models were evaluated concerning their accuracy, scalability, and flexibility within the context of collaborative filtering, content-based filtering, and hybrid techniques. Our investigation also includes cutting-edge methods for recommendation systems, including graph-based models and deep learning. The benefits and limitations of each approach are demonstrated, providing information on how AI may enhance personalized learning. Researchers and practitioners can get valuable insights from the analysis to improve the efficiency and efficacy of learning recommender systems.