Enhancing Education 4.0 with Artificial Intelligence

This study advocates for self-regulated learning in Higher Education 4.0, integrating smart technologies like sensors, wearables, analytics, and machine learning. It introduces an Early Recognition System driven by predictive analytics, inspired by N. A. Crowder's Auto Tutor theory. Personalized assessments and adaptive feedback empower students to steer their learning journey independently. This framework benefits students, professors, and administrators alike, fostering a dynamic educational environment. Enhanced data-driven decision-making leads to optimized resource allocation and program effectiveness, culminating in empowerment, engagement, and success for all stakeholders.