The volume of information that can be used in the development of knowledge bases that can be used in education is constantly increasing. Also, this amount of data is very difficult to process and store. When designing a knowledge base to optimize the educational process, it is important to use ontologies. At the moment, the creation of an ontological knowledge model is the most promising option for storing and processing information. The article describes effective approaches for generating an ontological model using machine learning models based on the Transformer model.

One of the ways to demonstrate subject-domain concepts is to let the learner play in the domain-related sandbox, while receiving the explanations of the domain laws that were broken when the user makes a wrong move. This can help the learners who understand definitions of the concepts poorly. We present two visual simulators for demonstrating concept properties and domain laws, implemented as web applications. They can be used to study programming, enabling trial-and-error learning, supported by the error messages, explaining why the user's action was wrong.