Abstract: The invention accelerates successful learning in a wide variety of existing and developing learning environments by generating the most effective dynamic adaptive tutoring tailored to a current learner model. It provides a full coverage of a basic tutoring functionality including passive and active tutoring manners, as well as presenting, testing and diagnosing modes. An innovative component of the invention, a unified generator of intelligent tutoring, deals exclusively with a logical aspect of tutoring leaving all media aspects to be realized by traditional components of tutoring systems. The generator represents a generic logical core (brain) of known specific intelligent tutoring systems comprising a reusable tutoring engine and a reusable tutoring knowledge/data framework including a reusable learner model.

Abstract: A computer implemented method and apparatus for simulating an intelligent tutor for interactive adaptive training of learners in any domain includes a Domain Module, a Tutor Module and an Interface. Items to be learned, and their prerequisite and other dependency relationships are represented in a fuzzy graph, together with a fuzzy logic computational engine, which dynamically adapts the available sequence of training actions (such as presentations/explanations, simulations, exercises and tasks/questions) to a current assessment of the learner's knowledge skill, the level of difficulty of the presented material, and preferences and learning style of the individual learner. Fuzzy logic is used as the basis of arc weightings, and the computations, but the general methodology is applicable to other approaches to weighting in computation.

Abstract: A computer implemented method and apparatus for simulating an intelligent tutor for interactive adaptive training of learners in any domain includes a Domain Module, a Tutor Module and an Interface. Items to be learned, and their prerequisite and other dependency relationships are represented in a fuzzy graph, together with a fuzzy logic computational engine, which dynamically adapts the available sequence of training actions (such as presentations/explanations, simulations, exercises and tasks/questions) to a current assessment of the learner's knowledge skill, the level of difficulty of the presented material, and preferences and learning style of the individual learner. Fuzzy logic is used as the basis of arc weightings, and the computations, but the general methodology is applicable to other approaches to weighting in computation.