Intelligent Tutoring Systems

Research on intelligent tutoring systems (ITS) has two aims: to provide sophisticated instructional advice on a one-on-one basis that is better than that achieved with conventional computer-aided instruction and is comparable to that of a good human tutor; and to develop and test models about the cognitive processes involved in instruction. The ‘intelligence’ of ITS comes from the application of artificial intelligence techniques which are used in four interacting components: The knowledge base contains the domain knowledge, the student model represents the student's current knowledge state, the pedagogical module contains suitable instructional measures which are contingent on the content of the student model, and the user interface enables an effective dialog between ITS and student. Usually, the knowledge base is the central part in the instructional process but there is a diversity of approaches that also put the emphasis on the other components. Although research on ITS has produced many interesting theoretical insights, there are relatively few ITS which are really used and there are very few which are regularly used in schools. This unsatisfactory state of affairs may be due to researchers' diversity of interests, missing evaluation studies that show the superiority of ITS, and theoretical problems with the student model. Current, more practically inclined, approaches de-emphasize the reliance on the problematic student model and put more effort in the construction of theory-based user interfaces.