In this paper, it is presented an intelligent tutoring system for signal-parametric fault-tolerant systems. The Technical Intellectual Tutoring System (TITUS) takes advantage of Bayesian networks (BNs), which are a formal framework for uncertainty management. It is discussed how it employs BNs as an inference engine to assess the students' mastery of knowledge and determine the proper pedagogical decisions for a productive learning process. In addition, it is described the architecture of TITUS and the role of each module in the system. TITUS has been tested and the experimental results are shown.

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