Pathways to Optimal Learning: Task Load-Driven Neuroadaptive Adaptation and Motivational Incentives

Katrina Sollazzo (), Alexander John Karran (), Thaddé Rolon-Merette (), Ioana Mihaela Stefanescu (), Constantinos Coursaris (), Pierre-Majorique Léger () and Sylvain Sénécal ()
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Katrina Sollazzo: HEC Montréal, Tech3Lab
Alexander John Karran: HEC Montréal, Tech3Lab
Thaddé Rolon-Merette: HEC Montréal, Tech3Lab
Ioana Mihaela Stefanescu: HEC Montréal, Tech3Lab
Constantinos Coursaris: HEC Montréal, Tech3Lab
Pierre-Majorique Léger: HEC Montréal, Tech3Lab
Sylvain Sénécal: HEC Montréal, Tech3Lab

A chapter in Information Systems and Neuroscience, 2025, pp 183-191 from Springer

Abstract: Abstract As online learning becomes increasingly prevalent, optimizing learning outcomes through personalized adaptation is crucial. This study compares the effects of a task load-driven neuroadaptive brain-computer interface to extrinsic motivation (EM) in enhancing learning outcomes. Using electroencephalography (EEG), task load (TL) is measured via paired frontal θ and parietal α activity, dynamically adjusting the learning interface. A three-group, between-subjects experiment (control, neuroadaptive, EM) assessed learning outcomes. Results suggest that while EM enhances performance, the neuroadaptive system effectively maintains participants in their optimal cognitive state without compromising performance. These findings highlight the potential of neuroadaptive systems in fostering personalized, effective learning environments.

Keywords: Neuroadaptive system; Brain-computer interface; Extrinsic motivation; Learning; Zone of proximal development; Electroencephalography (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnichp:978-3-032-00815-2_17

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DOI: 10.1007/978-3-032-00815-2_17

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