Digital learning systems rarely adapt to momentary cognitive state, creating risk of underload or overload. The current study investigates whether EEG-based closed-loop pacing can alter task load dynamics during a digital constellation-learning task. Fifty-one participants were randomly assigned to adaptive, reward, or control conditions.
In the adaptive condition, a task load index derived from frontal theta and parietal alpha adjusted feedback duration between 3 and 8 s; fixed 5s feedback was used in reward and control conditions, with reward participants expecting to receive performance-contingent draw entries. Accuracy increased across blocks in all groups, with no significant group or group-by-block effect. In contrast, EEG indices showed group-specific trajectories: task load decreased over time in the adaptive condition, and frontal theta and parietal alpha showed significant group-by-block interactions.
The adaptive condition also showed reduced late-session feedback duration, while reward participants reported higher satisfaction. Intrinsic motivation did not moderate group effects, although perceived competence predicted final-block performance and interest, effort, and competence predicted engagement. These findings suggest that EEG-based feedback pacing can regulate neurophysiological task load dynamics despite lack of immediate accuracy gains under the present task constraints.
The results support neuroadaptive scaffolding as a state-regulation mechanism for digital learning. This study was made possible thanks to funding from NSERC (RGPIN-2020-06048, ALLRP 565704–21) and SSHRC CGS-M. Tech3Lab, HEC Montréal, Montreal, Canada Katrina Sollazzo, Alexander John Karran, Thaddé Rolon-Merette, Alejandra Ruiz-Segura, Constantinos Coursaris, Pierre-Majorique Léger & Sylvain Sénécal The authors declare no competing interests.
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