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.
Keywords
Adaptive and Intelligent Web-based Educational Systems (AIWES), Machine learning (ML), Reinforcement Learning.
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Cite this article
Wang Dong, “Artificial Intelligence for Web-based Educational Systems”, Advances in Intelligent Systems and Technologies, pp. 055-065. 2022. doi:10.53759/aist/978-9914-9946-0-5_7
