Advancement in the Students’ Learning Potency and Machine Learning Insights: Designing the Nexus

Abstract

The advent of artificial intelligence is revolutionizing the world with cutting-edge technologies similar to machine learning, deep learning, computer vision, and robotics process automation. These innovations become enabling humans to minimize tangible exertion, optimize time, and achieve unparalleled precision in tasks that were previously challenging or impossible for individuals to perform. As research in this field continues to advance, a wide range of applications has emerged, including self-driving cars, facial recognition, emotion detection, speech recognition, and others. Here in this study, it has been developed an intelligent classroom system utilizing computer vision and emotion recognition to analyze multiple students' facial expressions, detect various emotions during lectures, and predict their performance in upcoming exams. Today, machines are increasingly assuming roles similar to humans in decision-making, recommendations, patient diagnosis, and recognition tasks. The future may soon see machines commanding humans, as ongoing research focuses on enabling machines to visualize, detect, and recognize individuals and objects through computer vision. Leveraging this technology, our research has led to the development of a deep learning-based system for recognizing students, interpreting their facial expressions, and gauging their attention levels.