ARTIFICIAL INTELLIGENCE TECHNOLOGIES IN THE DEVELOPMENT OF MANAGEMENT PERSONNEL COMPETENCIES
Keywords:
artificial intelligence, random forest, management personnel, competenceAbstract
The rapid development of digital technologies provides opportunities to organize the process of training, retraining, and upskilling personnel with innovative approaches. Considered one of the most effective technologies of the Fourth Industrial Revolution, artificial intelligence (AI) is fundamentally transforming the management of educational processes. In particular, these technologies enable the customization of education for each individual and the fulfillment of their needs on an individual basis. This article analyzes the experiences of advanced foreign countries in continuously developing the competencies of management personnel through flexible, remote, and electronic education, including the use of AI technologies. As a result of the research, the process of digitally transforming the development of competencies among management personnel active in the public service of the Republic of Uzbekistan is presented, along with the use of AI tools, the digital management of this system, and its economic efficiencies. In addition, the results of the initial diagnostic assessment of the competencies of more than 1,000 management personnel working in government agencies via an AI-based intellectual platform are analyzed, with their competencies classified according to the individual approaches required for their development
References
Aljuboori, A., F., Al-lawati, H. (2023). Intelligent
Adaptive E-Learning Systems: Current
Approaches, Architectures, and Applications.
Proceedings of the 22nd European Conferenceon e-Learning - ECEL 2023, Vol. 22(1), pp. 11-
https://doi.org/10.34190/ecel.22.1.1925.
Kamceva, E., Mitrevski, P. (2012). On the
General Paradigms for Implementing Adaptive
e-Learning Systems. ICT Innovations 2012 Web
Proceedings ISSN 1857-7288, Vol. 14(7), pp.
–289.
Vanegas, C. V., Puerta, J. E. A., Ceballos, M. N.,
& Sánchez, J. M. M. (2024). Personalized
Learning: an Adaptive Approach Based on the
VARK Model to Improve Distance Education.
Revista De Gestão Social E Ambiental, 18(12),
e010257.
https://doi.org/10.24857/rgsa.v18n12-046.
Kande, Sh., Goswami, P., Naul, G., Shinde, N.
(2016). Adaptive and Advanced E-learning
Using Artificial Intelligence. International
Journal of Engineering Trends and Applications
(IJETA), Vol. 3(2), pp. 34-37. ISSN: 2393-9516
www.ijetajournal.org. Published by Eighth
Sense Research Group.
Guettala, M., Bourekkache, S., Kazar, O., &
Harous, S. (2024). Generative Artificial
Intelligence in Education: Advancing Adaptive
and Personalized Learning. Acta Informatica
Pragensia, Vol. 13(3), pp. 460–489.
https://doi.org/10.18267/j.aip.235.
Bhaskaran, S., Swaminathan, P. (2014).
Intelligent Adaptive E-learning Model for
Learning Management System. Research
Journal of Applied Sciences, Engineering and
Technology, Vol. 7(16), pp. 3298-3303.
http://dx.doi.org/10.19026/rjaset.7.674.
Manoharan, A., Manoharan, D., R. (2024),
"Machine learning algorithms for personalized
learning paths", International Research Journal
of Modernization in Engineering Technology
and Science, Vol. 6 No. 3, pp. 211-218.
https://www.doi.org/10.56726/IRJMETS49965.
Asy’ari, M., & Sharov, S. (2024). Transforming
Education with ChatGPT: Advancing
Personalized Learning, Accessibility, and Ethical
AI Integration. International Journal of
Essential Competencies in Education, 3(2), 119-
https://doi.org/10.36312/ijece.v3i2.2424.
Billiot, T. (2023), "Continuous learning and
advancing technologies: a framework for
professional development and training in
artificial intelligence", Development and
Learning in Organizations, Vol. 37 No. 3, pp.
-31. https://doi.org/10.1108/DLO-04-2022-
Mao, J., Chen, B. & Liu, J.C. Generative Artificial
Intelligence in Education and Its Implications
for Assessment. TechTrends 68, 58–66 (2024).
https://doi.org/10.1007/s11528-023-00911-4.
Tapalova, O., and Zhiyenbayeva, N., 2022.
Artificial Intelligence in Education: AIEd for
Personalised Learning Pathways. The Electronic
Journal of e-Learning, 20(5), pp. 639-653.
Na, S. R. (2024). Application of Artificial
Intelligence in Employee Training and
Development. Mathematical Modeling and
Algorithm Application, 1(1), 26-28.
https://doi.org/10.54097/gg5eemnb.
Ramachandran, K. K., Srivastava, A., Panjwani,
V., Kumar, D., Cheepurupalli, N. R., & Mohan,
C. R. (2024). Developing AI-powered training
programs for employee upskilling and reskilling.
Journal of Informatics Education and Research,
(2), pp. 1186-1193.
https://doi.org/10.52783/jier.v4i2.903.
Tusquellas, N., Palau, R., Santiago, R. (2024).
Analysis of the potential of artificial intelligence
for professional development and talent
management: A systematic literature review.
International Journal of Information
Management Data Insights, 4 (2), pp. 1-9.
https://doi.org/10.1016/j.jjimei.2024.100288.
Casagranda, M., Colazzo, L., Molinari, A.,
Tomasini, S. (2010). E-Learning as an
Opportunity for the Public Administration. In:
Lytras, M.D., et al. Technology Enhanced
Learning. Quality of Teaching and Educational
Reform. TECH-EDUCATION 2010.
Communications in Computer and Information
Science, vol 73. Springer, Berlin, Heidelberg.
https://doi.org/10.1007/978-3-642-13166-
_61.
Yen, C.-J., Tu, C.-H., Sujo-Montes, L. E., Harati,
H., & Rodas, C. R. (2019). Using personal
learning environment (PLE) management to
support digital lifelong learning. International
Journal of Online Pedagogy and Course Design,
(3), 13-31.
https://doi:10.4018/IJOPCD.2019070102.
Dhupia, B., Alameen, A. (2019). Adaptive
eLearning System: Conceptual Framework for
Personalized Study Environment. In: Luhach,
A., Jat, D., Hawari, K., Gao, XZ., Lingras, P.
(eds) Advanced Informatics for Computing
Research. ICAICR 2019. Communications in
Computer and Information Science, vol 1075.
Springer
Downloads
Published
How to Cite
Issue
Section
License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.