Smart-Adaptive: Intelligent Recommendation Systems for Personalized Multi-Platform Learning

Conventional education systems have a standardized teaching methodology which fails to serve the needs of the different students when it comes to their unique learning requirements. The disparities in the level of understanding, previous knowledge, and the speed at which the learner perceives are usually ignored, and they may lead to diminished interest and disproportionate learning results. In order to meet this challenge, the present paper presents a Personalized Learning Platform that could be used in both web and mobile platforms where the material would be adjusted based on performance and learning behavior of the students. The proposed platform is an adaptive learning method based on AI to analyze interaction with students, assessment performance, and progress trends. According to this analysis, the system suggests appropriate learning materials and topics and especially on areas where the learner has been struggling. The platform helps with learning in the form of performance based content delivery, tracking of progress and personalized feedback