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Presently, many methods have been proposed to cater to online learning. To name some include blended learning, flipped learning, virtual and augmented reality, face recognition, gesture detection, chatbot assistance, and so forth. This agrees with the assertion that focuses on the ability of adoption to and smooth accommodation of various types of learners or students in on-campus classrooms and for students with remote or online classes (Bakken et al., 2020). Artificial Intelligence has already been applied to education primarily as a tool that helps develop skills and testing systems and can help fill need gaps in learning and teaching and allow schools and teachers to do more than ever before. Classroom discipline and management have taken a quantum leap in the past century away from the traditional model. The purpose of implementing classroom management strategies is to enhance prosocial behavior and increase student academic engagement (Dahlgren, n.d.). Educational data mining studies have been implemented to analyze student performance and prediction in classroom learning. Predictive modeling falls under AI, which can be used to accommodate all kinds of students. It is flexible enough to help all the students regardless of their learning speeds. It helps the learners move ahead only after they fully understand and grasp all the information they need. It analyzes the information teachers are using to determine whether the quality of the content meets the expected standards. Additionally, it also helps teachers monitor the progress of students on a personal level, thereby suggesting the best ways of teaching (Khan & Ghosh, 2021)(Romero & Ventura, 2013). However, it is also important for the teachers to be able to monitor the students' performance during class times and to be able to know whether they are understanding what the teachers are saying or they are just “daydreaming” as it can help them to or improve or possibly change their teaching techniques and methods, so to attain this objective, the teachers need some tools that can help them monitor the students' interaction during classes by watching their face and body movements and predicting their understanding levels, such as predictive models (Yang, 2000). Machine Learning and Predictive Modeling provide solutions to organizations worldwide for their own needs. However, predictive models with face detection and body gestures can be generated by extracting some dynamic visual processes, as in sign language recognition, where the movements of hands and body can give different meanings (Priya Pedamkar, 2020).
Machine learning has been used in the security field as well such as malware detection, in which a novel monte-carlo simulation-based model was used (Naveed et al., 2020). It uses a simulation based model called Heuristic-based Generative model that generalizes the attack patterns and then predicts any new unknown attacked and then detects and flags them in real-time with a high accuracy.