Closed-loop EEG feedback pacing alters task load during digital learning

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.