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In this paper we present a physiological computing approach based on electroencephalogram (EEG) signals to adaptively sequence the learning content according to the learners’ mental states. The system draws on techniques from Brain Computer Interface and educational psychology to automatically select the next best learning activity according to changes in the learners’ mental states such as attention and workload. The objective of this system is to maintain the learner in a positive mental state throughout the tutoring session.

Intelligent tutoring system; Engagement; Workload; Real-time adaptive system; EEG; Machine learning; Experience and affect