Human-machine interaction with intelligent machine learning algorithms for smart classrooms

Abstract

ABSTRACT The modern technology of learning and teaching in education has moved on to smart classes. The introduction of artificial intelligence (AI) in the education sector has essentially taken over traditional classrooms and transformed how education leads to admiration. Various data types, such as videos, images, sound, text, and biometric data like eye-tracking potentially captured through smart classrooms, pose new issues and challenges for artificial intelligence algorithms. Hence, this paper suggests the Machine Learning Assisted Intelligent Human-Machine Interaction (MLIHMI) model for the smart classroom monitoring system. The real-time interactive dimension signifies the capability to support the Smart classroom’s teaching and human-computer interactions, including smooth interaction, convenient operation, and interactive monitoring. This paper used ML and HMI models to introduce a new effective database for online learning and smart classroom environments utilising the learners’ body postures, facial expressions, and hand gestures. The numerical findings show that the suggested system achieves less error rate of 10.1% and enhances the accuracy rate of 96.5%, with the highest classification ratio of 95.6%, prediction rate of 93.6%, the detection rate of 97.3%, and engagement rate of 81.3% in student gesture and postures recognition and the identification of facial expressions with the high performance of 93.3% when compared to other existing models.