Brain-Based Intelligent Tutoring Systems

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Our objectives are inspired by some results we have obtained from previous experiments on subconscious learning . In these works we have discovered that brainwaves analysis allowed to distinguish a change, at a moment, between cognitive reasoning and intuitive reasoning, in other words from conscious to subconscious mode. This change is similar to what is observed when learners switch from a cognitive task to gaming. Instead of continuing to find a solution, they play with the system until they encounter a solution but just by trial and error without any cognitive effort. This leads to an important problem in knowledge acquisition which corresponds to a cerebral state: engagement or disengagement. In the first one, the learner is interested (motivated) to explore the possible solutions and could converge to the final solution. In the second one, the learner is no more interested to continue on the task (which sometimes explains the derivation to gaming). Our experiments highlighted also two typical kinds of reasoning: cognitive reasoning and intuitive reasoning. While the first involves generally the conscious part of the brain, the second is more linked to the subconscious part. Sometimes, a problem solving situation will involve successively cognitive and intuitive reasoning, switching back and forth between the two modes. In both cases emotions play an important role to facilitate or not learning. The emotional conditions of the brain allow for the transmission of information not only to the conscious part but also to subconscious region and this last one seems to contain more knowledge and to process the information at a higher speed. As we have seen in our previous studies, the affective part is extremely important as it prepares the brain to accept or not the information. Intuitive behaviour is more present in video games. For instance, in flight training or games the resulting experience and abilities will concern immediate reactions (reflexes) Following our previous works, we want to detect more precisely emotional conditions and cerebral states that lead to engagement or disengagement. This implies to detect the emotional condition (for instance a learner is excited or not) and learning condition (the learning objectives are too high) in which learning occurs, and measure the resulting cerebral state (for understanding more clearly the different steps of learning and the evolution of cerebral states). Moreover, a continuous control of learner’s brain activity could be used by an intelligent system to give advices to the learner and put him into the best emotional conditions for learning. As we can see the brain is at the center of this intelligent system. An intelligent personal adviser (agent) could follow the evolution of cerebral states and provide personalized advices to optimize learning. To reach this goal, we need to explore more possibilities to detect the different steps of engagement and disengagement (when a learner cannot understand the problem and find the solution) through EEG signals. The main problem remains to detect and monitor the learner emotional state with precision and in real time. Emerging device allow to have a direct interaction between the learner and his environment (eye tracking systems, brainwaves interaction with helmets) The general objectives of our research are (1) to build an integrated interactive Brain-based environment able to detect the conditions of engagement and disengagement both in cognitive and intuitive learning,using a video game, and (2) to build a complete Brain-based video game learning environment able to provide the best pedagogical strategies to foster learner’s memorization and knowledge acquisition.