Artificial Intelligence-Enabled Interactive System Modeling for Teaching and Learning Based on Cognitive Web Services

Abstract

The future of modern education and web-based learning is inherently associated with the advancement in modern technologies and computing capacities of new smart machines, such as artificial intelligence (AI). AI is a high-performance computing environment powered by special processors that use cognitive computing for machine learning and data analytics. There are major challenges in online or web-based learning, such as flexibility, student support, classification of teaching, and learning activities. Hence, this paper proposes smart web-based interactive system modeling (SWISM)based on artificial intelligence for teaching and learning. The paper aimed to categorize learners according to their learning skills and discover how to enable learners with machine learning techniques to have appropriate, quality learning objects. Furthermore, local weight, linear regression, and linear regression methods have been introduced to predict the student learning performance in a cloud platform.