Traditional Learning Management Systems (LMS) often provide the same educational materials to everyone, overlooking each learner's unique pace, interests, and achievements. This can lead to lower motivation and less effective results in education. In this paper, we introduce an innovative AI-powered Learning Management System that employs Machine Learning methods, particularly Neural Collaborative Filtering (NCF), to offer tailored learning suggestions. Our system actively examines learner actions, including how they engage with videos, perform on quizzes, express topic preferences, and track their overall progress, to create individualized learning journeys. By representing both learners and educational resources in a common hidden space, it effectively forecasts the most suitable content for each person. The LMS features an AI-powered assistant that functions like a personal tutor, crafting flexible learning plans and custom quizzes using live performance insights. As learners interact more with the system, the recommendation tool gets smarter, delivering suggestions that are timely, pertinent, and aligned with their objectives. It caters to diverse subjects such as coding, software creation, language acquisition, and hands-on project work. Through rigorous testing, we've shown that this method boosts learner involvement, refines recommendation precision, and enhances overall learning productivity when compared to conventional, unchanging LMS setups. The design is built to grow with demand, adjust to changes, and thrive in today's online learning landscapes.
N et al. (Wed,) studied this question.