Abstract:
In order to match better the employers requirements and increase the universities competitiveness
providing economic and engineering education, it is proposed to use new tools for training students, such
as individual trajectories and other forms of training individualization. It is important to take into account the requirements of Federal state educational standards (FSES). However, the FSES retains
enough alternatives for individualizing training in specialization choosing in a certain professional field.
For example, for students studying information technologies such specialization may be in the choice between different economic sectors: banks, telecommunications, industrial production, logistics, automotive
industry, Internet companies, social networks, etc. Individualization of training may consist in a more
detailed study of one of the areas in it: databases; expert systems; distributed registries; artificial intelligence; image recognition; natural language understanding; automated systems and technological processes management; robotics, etc. Opportunities for individualizing student learning be even within the
FSES. Examples of training individualization of BMSTU students are presented.
Practical work has shown that individualization complicates the work and increases the time spent by
University staff on managing trajectories in student training. Achievements of mivar technologies of logical artificial intelligence allow automating routine operations for managing individual students trajectories. In general, artificial intelligence can help in almost all tasks of economic and engineering education
in the transition to continuous training of people "through all life".
Keywords:individualization of training, motivation, life strategy, elective disciplines, artificial intelligence, economic systems, mivar, mivar nets, expert systems, decision-making systems, robots.
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