This paper considers the problem of Internet and more general cyber addiction of primary school children. The aim is to investigate the level of dependency of young children in Internet, computer games and devices. The authors work is part of a project work researching digital skills and media-education at early age. A survey with 274 fourth grade students is described and analysed in the context of the tendency to meet and fall in love with the screen from a younger age. Young children do not recognise the eager to take some digital device at hand as a problem. They cannot really estimate the time spent on games or other online activities. Theoretical review of the problem is done, and the basic aspects of cyber addiction are mentioned. The authors’ point of view is that higher digital skills at early age could be a factor for avoiding higher cyber addiction of the children. Keywords: Cyber addiction, early age, digital skills, primary school, dependency.

New trends in the development of society in the digital age determine the growing role of artificial intelligence (AI). This determines the need for its formal and informal study in different levels and in different volumes in both classroom and extracurricular activities of bulgarian schools. On the basis of a curriculum approved by the Ministry of Education and Science, the development of appropriate teaching resources and aids began. In this report, the authors will present an approach to presenting knowledge with general meaning in artificial intelligence by applying logical inferences, rules and dependencies. The main topics of the curriculum and the way of their structuring will be presented. The main idea is, following the requirements of classical artificial intelligence, to consider different methods and approaches for presenting this theory in the context of high school education.

Abstract. In this paper we deal with performance analysis of Monte Carlo algorithm for large linear algebra problems. We consider applicability and efficiency of the Markov chain Monte Carlo for large problems, i.e., problems involving matrices with a number of non-zero elements ranging between one million and one billion. We are concentrating on analysis of the almost Optimal Monte Carlo (MAO) algorithm for evaluating bilinear forms of matrix powers since they form the so-called Krylov subspaces.Results are presented comparing the performance of the Robust and Non-robust Monte Carlo algorithms. The algorithms are tested on large dense matrices as well as on large unstructured sparse matrices.

The paper presents some accents concerning implementation of the STEM approach in education, such as the learning environment, content, technologies and the management process. Special accent is set on the use of school robots in interdisciplinary school activities. Examples from pedagogy practice are presented. Some conclusions are formulated.