Artificial Intelligence for Web-based Educational Systems

Abstract

Due to the global COVID-19 epidemic in the preceding two years, there has been a significant debate among different academics about how learners may be lectured through the web while maintaining a higher degree of cognitive efficiency. Students may have problems concentrating on their work because of the absence of teacher-student connection, but there are benefits to online learning that are not offered in conventional classrooms. The Adaptive and Intelligent Web-based Educational Systems (AIWES) is a platform that incorporates the design of students' online courses. RLATES is an AIWES that uses reinforcement learning to build instructional tactics. This research intends the aggregation and evaluation of the present research, model classification, and design techniques for integrated functional academic frameworks as a precondition to undertaking research in this subject, with the purpose of acting as an academic standard in the related fields to aid them obtain accessibility to fundamental materials conveniently and quickly.