Enhancing learning experiences: EEG-based passive BCI system adapts learning speed to cognitive load in real-time, with motivation as catalyst

Beauchemin, Noémie; Charland, Patrick; Karran, Alexander; Boasen, Jared; Tadson, Bella; Sénécal, Sylvain; Léger, Pierre-Majorique · 2024 · Crossref

DOI: 10.3389/fnhum.2024.1416683

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Summary

This study addresses the limitation of current Computer-Based Learning (CBL) environments, which typically employ a "one-size-fits-all" approach that fails to account for individual learners' real-time cognitive states. Motivated by the Zone of Proximal Development (ZPD) and Cognitive Load Theory, the research investigates whether an EEG-based passive Brain-Computer Interface (BCI) can enhance learning experiences by adapting the speed of information presentation to the learner’s real-time cognitive load. Additionally, the study explores the moderating role of motivation, questioning whether motivational factors are necessary for effective BCI adaptation. The experimental design involved three groups: a non-adaptive control group, an adaptive BCI group, and an adaptive BCI group with an added motivational catalyst. Participants first completed an n-back calibration task to establish baseline cognitive metrics. They then engaged in a memory-based learning task involving astrological constellations. The BCI system monitored brain activity via EEG, specifically analyzing theta and alpha oscillations to estimate cognitive load, and dynamically adjusted the presentation speed of learning materials to maintain an optimal cognitive state. Learning outcomes were measured through performance on the learning task, while subjective experiences—including self-perceived mental workload, cognitive absorption, and satisfaction—were assessed via post-test questionnaires. Statistical analyses, including Mann–Whitney tests, were used to compare between-group differences. The results indicated that combining the BCI system with motivational factors led to significantly greater learning gains and an improved overall learning experience compared to other conditions. However, no significant difference was found between the adaptive BCI group without motivation and the non-adaptive control group regarding overall learning gains, self-perceived mental workload, or cognitive absorption. Notably, participants in the non-adaptive group, who faced an imposed learning pace, reported higher overall satisfaction but also experienced higher levels of temporal stress. These findings suggest that while the BCI system is feasible for memorization-based learning, its effectiveness in enhancing learning gains is contingent upon the presence of motivational elements. The study concludes that passive BCI systems hold potential for optimizing learning experiences by tailoring instructional pace to cognitive load, but motivation acts as a critical catalyst for realizing these benefits. The research highlights the importance of integrating psychological factors like motivation into neuroadaptive interfaces. The authors recommend future work to optimize the BCI’s adaptive index and to test the generalizability of these findings across different learning contexts and subject matters. This work contributes to the field by demonstrating the practical applicability of closed-loop BCI systems in education, moving beyond theoretical frameworks to empirical validation of real-time neuroadaptive learning environments.

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• neuro workload indices