An Intelligent e-learning Scenario for Knowledge Retrieval

Abstract

Electronic learning (E-Learning) is an invaluable tool for businesses looking to boost employee productivity and academic achievement, as well as for schools looking to improve the quality of its student body and faculty. Educational institutions rely on effective information retrieval as a tool to help students improve their abilities and throughout the learning process. When 
dealing with information retrieval in the classroom, both students and teachers encounter several challenges. Research shows that there is still a need for a more effective strategy. Technologies like Semantic and Artificial Intelligence hold great promise 
for meeting the demands of e-Learning. Problems with storage resources and information retrieval are both exacerbated by the merging of e-Learning and digital libraries. Digital libraries are now far more flexible places to study and gain knowledge because to readily available infrastructure, time flexibility, learning materials, and ways to share them. Our research aims to create a broad platform for learning environments via the presentation of an e-learning management system for the Digital Library at Seville University. Possible intelligent infrastructures for building decentralized digital libraries with a global semantic schema are explored from a search viewpoint. We propose a framework for an intelligent search engine that is both semantic and built on concepts. In this group effort, we provide an innovative way for engineering students to engage with an online learning platform that takes into account each learner’s unique characteristics and physical environment. By analyzing recorded semantic metadata and using AI technology, we provide a thorough method for finding information items in massive digital collections.