OPTIMIZING VISUOSPATIAL WORKING MEMORY THROUGH ADAPTIVE DIGITAL LEARNING: BEHAVIORAL AND EEG EVIDENCE

The widespread integration of digital screens into education raises critical questions regarding their impact on essential cognitive functions such as working memory. Standard digital learning interfaces are typically static and rarely adapt to the learner’s fluctuating cognitive state, which may lead either to cognitive overload or, conversely, to disengagement due to under-stimulation. This study investigates whether an adaptive learning system that dynamically adjusts task difficulty can optimize working memory performance compared with a conventional digital learning format.

An experimental protocol was developed to compare behavioral performance and electroencephalographic (EEG) activity in students performing a visuospatial working memory task (Corsi Block-Tapping Test) under two conditions: (1) a Standard condition, in which task difficulty remained fixed, and (2) an Adaptive condition, in which task difficulty was continuously adjusted in real time. The adaptive condition was designed to maintain participants within an optimal range of cognitive effort.

We hypothesize that the adaptive condition, by preventing both cognitive overload and boredom, will lead to significantly higher behavioral performance than the standard condition. At the neurophysiological level, we predict that this optimized learning state will be associated with specific modulations of brain activity, including a reduction in neural markers of cognitive load (frontal theta power) and sustained attentional engagement reflected by parietal alpha desynchronization.

This work aims to provide neuroscientific evidence supporting the relevance of personalized adaptive learning systems. The findings may inform the design of future educational technologies that better align instructional demands with learners’ cognitive capacities.