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.