Cognitive Computing Models in Artificial Intelligence Education: From Theory to Practice

Abstract

Cognitive computing has emerged as a transformative force in artificial intelligence (AI) education, bridging theoretical advancements with practical applications. This article explores the role of cognitive models in enhancing learning systems, from intelligent tutoring and personalized recommendations to virtual laboratories and special education support. It examines key technologies—such as knowledge graphs, natural language processing, and multimodal data analysis—that enable adaptive, human-like responsiveness. The study also addresses technical challenges like interpretability and data privacy, alongside ethical concerns including equity and bias. Looking forward, it discusses how cognitive computing could reshape future learning modalities and aligns with trends like artificial general intelligence and interdisciplinary learning science. By tracing the path from theory to practice, this work underscores the potential of cognitive computing to create an inclusive, dynamic educational landscape, while highlighting the need for ethical and technical rigor to ensure its responsible evolution.