Decoding Cognitive Load: Eye-Tracking Insights into Working Memory and Visual Attention

Understanding and classifying cognitive workload is a critical challenge in educational technology and human-computer interaction (HCI). While cognitive load is often treated as a single, generalized concept, its nuanced components—such as working memory load and visual attention load—play distinct roles in learning environments. To investigate these differences, we conducted a controlled experiment, collecting a comprehensive eye-tracking dataset comprising 528,017 data points across varied cognitive tasks. Leveraging machine learning, we demonstrate that these cognitive states can be classified, revealing measurable distinctions between load types. Our findings pave the way for adaptive learning systems that dynamically tailor instructional content based on cognitive state assessments. This research contributes to the development of personalized, AI-enhanced educational tools, advancing both theoretical understanding and practical applications of eye-tracking in education, cognitive assessment, and HCI.