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Technologies of improving the university efficiency by using artificial intelligence: motivational aspect

Author

Listed:
  • Mikhail V. Vinichenko

    (Russian State Social University, Russian Federation)

  • Alexander V. Melnichuk

    (Russian State Social University, Russian Federation)

  • Peter Karácsony

    (J. Selye University, Slovakia)

Abstract

The aim of this study was to identify the most appropriate technologies to improve the university efficiency by using the motivational artificial intelligence (AI). Methods of the study were as follows: the questionnaire survey by using the Google Chrome electronic service, content analysis, methods of statistical analysis, and a focus group. The authors’ version of the questionnaire was made by using the Likert methodology taking into account indicators of the QS World University Rankings rating system. The data obtained during the three stages were generalized and analyzed by using the descriptive statistics. The regression analysis was used to study the relationship between the motives of the academic staff (AS) and the nature of the stimulating effect of the university authorities on the staff of the university. Results: The discrepancy between the AS motivation structure and the range of stimulation methods applied by the university authorities, a continuous increase in the burden from introduced innovations, and the formal style for employees to fulfill new tasks have been revealed. The analysis of the results on using the techniques and methods by the university authorities to motivate and stimulate the staff has shown the need in new combinatorics, an innovative system that harmoniously combines the advantages of natural and artificial intelligence to motivate the AS in training HR for the digital economy of the 21st century. The new system should be universal and flexibly respond to constant changes in the socio-economic environment. It is important to timely eliminate the contradictions in needs and teachers’ opinion on the ideal assessment system of their activities and offered forms of stimulation by universities authorities. The vectors of their activities must be constantly coordinated, based on the AI capabilities. The introduction of AI in the activities of universities improves the competitiveness of promising, innovative teachers and has positive impact on the image, efficiency, academic reputation, and citation index of universities. The authors for the first time ever have studied the problems of using the AI in the motivational system of the university’s AS and offered technologies to improve the efficiency of universities by using the motivational AI. The practical importance of solving the problem is related to the real possibility of applying the offered technologies by the university authorities that strive to improve their efficiency and competitiveness in the educational market. The main advantage of the work is related to the advanced solutions of the emerging problems on using the AI in motivating the university staff identified during the three-stage study. The interdisciplinary nature of the study and the offered technologies can serve as the basis for the further study and an additional element that expands the views, approaches, and the framework of categories and concepts of the world science. Conclusion: The most suitable technologies for the university that strives to be efficient include the elimination of the imbalance in the system of staff motives – incentives of the university (employer) authorities, the harmonious use of the AI in educational activities and the system of motivation and stimulation of staff where the natural intelligence prevails, and the improvement of the staff’s publication and grant activities by using the AI with a synergistic effect due to efficient team building.

Suggested Citation

  • Mikhail V. Vinichenko & Alexander V. Melnichuk & Peter Karácsony, 2020. "Technologies of improving the university efficiency by using artificial intelligence: motivational aspect," Entrepreneurship and Sustainability Issues, VsI Entrepreneurship and Sustainability Center, vol. 7(4), pages 2696-2714, June.
    • Handle: RePEc:ssi:jouesi:v:7:y:2020:i:4:p:2696-2714
    DOI: 10.9770/jesi.2020.7.4(9)
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    References listed on IDEAS

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    2. Yazdani, Morteza & Pamucar, Dragan & Erdmann, Anett & Toro-Dupouy, Luis, 2023. "Resilient sustainable investment in digital education technology: A stakeholder-centric decision support model under uncertainty," Technological Forecasting and Social Change, Elsevier, vol. 188(C).
    3. Gábor Szabó-Szentgróti & Bence Végvári & József Varga, 2021. "Impact of Industry 4.0 and Digitization on Labor Market for 2030-Verification of Keynes’ Prediction," Sustainability, MDPI, vol. 13(14), pages 1-19, July.
    4. Igor Denisov & Yelena Petrenko & Irina Koretskaya & Stanislav Benčič, 2021. "The Gameover in Universities Education Management during the Pandemic COVID-19: Challenges to Sustainable Development in a Digitalized Environment," Sustainability, MDPI, vol. 13(13), pages 1-23, July.
    5. Ramona Simut & Ciprian Simut & Daniel Badulescu & Alina Badulescu, 2024. "Artificial Intelligence and the Modelling of Teachers’ Competencies," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 26(65), pages 181-181, February.

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    More about this item

    Keywords

    technologies; motivation and stimulation; Artificial Intelligence (AI); efficiency; higher education;
    All these keywords.

    JEL classification:

    • O31 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Innovation and Invention: Processes and Incentives
    • O34 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Intellectual Property and Intellectual Capital

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