Introduction Cognitive scientists have been, for years, searching for essential ingredients of intelligence. Although this issue may not be satisfactorily resolved for quite some time, two abilities are clearly central to intelligent behaviour. One is the ability to acquire knowledge or skill through experience; that is, the ability to learn. The second is the ability to apply the knowledge or skill possessed to solve new problems; that is, the ability to reason. The new problems may concern actual events in the real world: for example, when one has to react to a new external stimulus; or may be imaginary, for instance, when one creates them for planning purposes. A precondition for the above abilities is the capability to represent diverse forms of knowledge. As our knowledge is built of individual concepts, to represent knowledge one needs to represent concepts. Consequently, understanding how concepts are represented is a fundamental problem underlying all efforts in t

This chapter presents how Machine Learning Techniques can effectively contribute to improve the quality of interactions in Guided Discovery Tutoring Environments (GDTE) . We review several approaches to integrate Machine Learning in ITS. Most of these approaches use concept learning from examples to maintain a Student Model. We go along presenting an alternative use of induction techniques to learn concepts on the same data that are presented to the learner. We present on a concrete example how this approach is integrated in a GDTE called MEMOCAR, a Computer Aided Language Learning System for Chinese characters. Three main types of activity are identified in MEMOCAR: familiarization with Chinese characters, collaborative discovery of similarities between characters and exercises to test characters acquisition. The stage of familiarization is supported by exploration of hyperdata whilst collaborative discovery and exercises' diagnosis are supported by a tool based on CHARADE, a top-down...