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

Introduction

Artificial intelligence (AI) has been a significant focus in recent years in the modern teaching field, with the hope that it remains one of the key goals for future-oriented schools and educators (Hinojo-Lucena et al., 2019). It facilitates individualized learning and real-time feedback. Several AI-enabled applications and programs allow students to better understand and integrate concepts in areas for learning (Yoon et al., 2019). The stages vary from class assistants with AI to insightful. AI systems that best track the success and reversal of an individual student (Haristiani et al., 2019). AI will push performance, optimization, and streamline admin tasks to provide teachers with flexibility, independence (Miyaji et al., 2019), and comprehension of special capabilities for human machines (Abdel‐Basset et al., 2019). The AI view in education strives together for the best outcomes for students, incorporating computers and teachers' best abilities (How et al., 2019). At every level of education, information and communication technologies (ICTs) play a critical role in enhancing learning and teaching in the SWISM framework. In developed countries, it is widely accepted that ICTs have enhanced educational standards based on cognitive web services for machine learning and data analytics (Shakeel et al., 2020). Artificial intelligence technologies can assist people with language differences or impaired hearing or visual access to global classrooms (Yoo et al., 2019). AI helps break down silos between schools and classrooms (Kumar et al., 2019). An instructor needs much time to assess homes and assessments (Acevedo et al., 2018). At the same time, AI joins and works efficiently on these assignments while guiding the learning gaps (Selvakumar et al., 2016). It is proposed in this paper that Smart Web-based Interactive System Modeling (SWISM) can be used to overcome these problems. Teachers and reflection facilitators could seamlessly access students' digital devices via cloud platforms. Teachers can use cognitive web services for machine learning and data analytics to encourage students to engage in reflective activities.