RACE: A Real-Time Architecture for Cognitive State Estimation, Development Overview and Study in Progress

Cognitive load management is important in successful learning, referring to working memory and other factors related to accomplishing instructional tasks. Cognitive overload and underload are induced when challenges provided to the student exceed or underutilize working memory capacity, leading to suboptimal learning. The link between cognitive load and successful learning is well established. However, current educational technologies fail to utilize cognitive load effectively to personalize learning and fail to adapt to the student's learning pace. Neuroadaptive interfaces, specifically Brain-Computer Interfaces, are slowly transforming the traditional educational landscape offering promising possibilities to enhance and improve learning experiences by enabling direct communication between the brain and a computer to adapt instructional content in real-time based on the assessment of cognitive load brain states. This research-in-progress paper discusses the development, following a design science research methodology, of RACE: a novel artefact consisting of a Closed-Loop Brain-Computer Interface that measures cognitive load in real-time applied to a memorization-based learning task to adapt the learning Interactive User Interface in real-time based on assessed and classified levels of cognitive load. Specifically, this artefact adapts the speed of information provision and response time to the learner's pace to make learning more personalized and effective.