Modeling and improving the efficiency of educational sciences using meta-heuristic algorithms

Regression analysis was used to investigate the relationship between the motivations of academic staff and the nature of the motivating effect of university officials on university employees. Results: The discrepancy between the motivation structure of AS and the range of stimulation methods applied by the university authorities, the continuous increase of the load caused by the introduced innovations and the official style of the employees to perform new tasks have been revealed. The analysis of results on the use of techniques and methods by university authorities to motivate and stimulate employees shows the need for new combinations, an innovative system that harmoniously combines the advantages of natural and artificial intelligence to motivate AS in Human resource training combines. Digital Economy of the ۲۱st Century The new system must be global and flexibly respond to ongoing changes in the socio-economic environment. It is important to timely eliminate conflicts in the needs and opinions of teachers about the ideal evaluation system of their activities and the forms of stimulation provided by the university authorities. The vectors of their activities must be continuously coordinated based on the capabilities of artificial intelligence. The introduction of artificial intelligence in the activities of universities improves the competitiveness of promising and innovative teachers and has a positive effect on the image, efficiency, scientific reputation and citation index of universities. For the first time, the authors have investigated the problems of using artificial intelligence in the AS motivational system of the university and presented technologies to improve the efficiency of universities using motivational artificial intelligence. The practical importance of solving the problem is related to the real possibility of applying the technologies provided by university officials who are trying to improve their efficiency and competitiveness in the educational market.The most suitable technologies for a university that strives for efficiency include eliminating the imbalance in the system of employee incentives - the incentives of university officials (employer), the coordinated use of artificial intelligence in educational activities and the incentive system. and motivating employees where natural intelligence prevails and improving employee release and award activities using artificial intelligence with a synergistic effect due to efficient team building.

Keywords:

meta-heuristic algorithms - optimization - artificial intelligence , Efficiency , Higher Education