A computational model for causal learning in cognitive agents

To mimic human tutors and provide optimal training, a cognitive tutoring agent should be able to continuously learn from its interactions with learners. An important element that helps a tutor better understand learner’s mistake is finding the causes of the learners’ mistakes. In this paper, we explain how we have designed and integrated a causal learning mechanism in a cognitive agent named CELTS (Conscious Emotional Learning Tutoring System) that assists learners during learning activities. Unlike other works in cognitive agents that used Bayesian Networks to deal with causality, CELTS’s causal learning mechanism is implemented using data mining algorithms that can be used with large amount of data. The integration of a causal learning mechanism within CELTS allows it to predict learners’ mistakes. Experiments showed that the causal learning mechanism help CELTS improve learners’ performance.