Mobile EEG (DreamMachine) and AI in Education: Toward Smarter Classrooms and Better Mental Health.

Abstract

The convergence of mobile electroencephalography (EEG) technology and artificial intelligence (AI) offers transformative potential for education. We propose a novel conceptual framework that integrates DreamMachine, a clinically validated mobile EEG device, with AI-driven adaptive learning systems. Our vision is to create neuroadaptive educational environments where real-time EEG signals, including markers of attention, cognitive load, and emotional states, and mental well-being inform AI algorithms to personalize content delivery dynamically. Such an approach could significantly enhance learning efficiency, engagement, and inclusivity, and support the mental health of learners by identifying stress or cognitive overload early and enabling timely, personalized interventions. This study outlines the technical feasibility of leveraging DreamMachine's high-fidelity, low-cost, portable EEG data in the classroom and remote settings. It proposes a machine-learning pipeline for real-time cognitive state detection. Ethical considerations surrounding neurodata use in education are discussed, emphasizing the need for privacy, transparency, and student agency. We invite collaboration on this interdisciplinary initiative, aiming to pilot the system in educational settings and redefine the future of personalized, mentally supportive learning.