Revolutionizing Learning Experiences by Enhancing Learning Proficiency Using Artificial Intelligence and Human-Computer Interaction

Abstract

This research chapter explores the collaborative integration of electroencephalography (EEG), artificial intelligence (AI), machine learning (ML), and pedagogy to revolutionize human activity recognition within educational settings. A primary focus lies in the utilization of ML models to scrutinize EEG data, presenting a groundbreaking approach for the early detection and classification of neurological disorders. The study reveals promising correlations between cognitive performance and character skills, unraveling their pivotal roles in shaping learning behavior. Furthermore, the investigation assesses the transformative impact of virtual reality (VR) on cognitive load within multimedia learning environments, shed- ding light on the intricate dynamics that VR introduces to the educational landscape. The utilization of brain-computer interfaces (BCIs) in mainstream education emerges as a key exploration, showcasing the potential of BCIs to bridge the gap between technological innovation and traditional learning methodologies. It includes the analysis of cognitive load, the examination of environmental and postural effects on learning outcomes, the development of robust seizure detection systems, and the evaluation of student engagement in online learning platforms. The research findings collectively offer a holistic understanding of how integrated technologies can not only enhance educational practices but also pave the way for a more personalized and adaptive learning experience. This study thus underscores the transformative potential of combining neuroscience, AI, and pedagogy to shape the future of education.