331 Copyright © 2019, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. Chapter 12 DOI: 10.4018/978-1-5225-7709-6.ch012 ABSTRACT In this chapter, the authors present a methodology for developing a model-tracing cognitive tutor. The methodology is based on Bayesian probabilistic networks for generating pedagogical interventions. The presented probabilistic model increases fdelity assessment due to its ability of independently diagnos- ing the degree of mastery for every knowledge component involved in students’ actions; fdelity assess- ment in education is the ability to represent students’ cognitive states as close as possible for analysis and evaluation. The cognitive tutor was developed to promote a self-regulated learning approach with an open learner model. The open learner model let students change the learning fow by changing the assigned tasks. The authors explain in detail the structural construction and employed algorithms for developing a model-tracing cognitive tutor in the domain of fault-tolerant systems. Preliminary results and future work are also discussed to assess efectiveness of the proposed approach and its implication in actual educational programs. INTRODUCTION In this chapter, we discuss several concepts and their implementation for developing a Model-tracing cognitive tutor (MTCT). An Open learner model (OLM) is featured to support student self-assessment for promoting development of “help-seeking” meta-cognitive skills. This is based on the hypothesis about Information Technologies for Learning Principles of Fault-Tolerant Systems Juan Pablo Martínez Bastida National Aerospace University – Kharkiv Aviation Institute, Ukraine Olena Havrylenko National Aerospace University – Kharkiv Aviation Institute, Ukraine Andrey Chukhray National Aerospace University – Kharkiv Aviation Institute, Ukraine