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

Noémie Beauchemin (), Alexander John Karran, Jared Boasen, Bella Tadson, Patrick Charland, François Courtemanche, Sylvain Sénécal and Pierre-Majorique Léger ()
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Noémie Beauchemin: HEC Montréal
Alexander John Karran: HEC Montréal
Jared Boasen: HEC Montréal
Bella Tadson: HEC Montréal
Patrick Charland: Université du Québec à Montréal
François Courtemanche: HEC Montréal
Sylvain Sénécal: HEC Montréal
Pierre-Majorique Léger: HEC Montréal

A chapter in Information Systems and Neuroscience, 2024, pp 9-20 from Springer

Abstract: Abstract 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.

Keywords: Neuroadaptive interface; Brain-computer interface; Biocybernetic loop; Learning; Cognitive load theory; Design science (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/978-3-031-58396-4_2

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