| Issue |
E3S Web Conf.
Volume 461, 2023
Rudenko International Conference “Methodological Problems in Reliability Study of Large Energy Systems“ (RSES 2023)
|
|
|---|---|---|
| Article Number | 01092 | |
| Number of page(s) | 6 | |
| DOI | https://doi.org/10.1051/e3sconf/202346101092 | |
| Published online | 12 December 2023 | |
AI-powered learning: revolutionizing technical higher education institutions through advanced power supply fundamentals
1 Tashkent State Technical University named after Islam Karimov, 100095, Uzbekistan, Tashkent, University St. 2A
2 Karakalpak State University, Nukus, 230101, Uzbekistan
3 “TIIAME” NRU Bukhara Institute of Natural Resources Management, Bukhara, 105009, Uzbekistan
4 Bukhara state university, 105009 Bukhara, Uzbekistan
* Corresponding author: nomon.niyozov_2422@mail.ru
The burgeoning technological education landscape mandates a reevaluation of educational methodologies, especially in pivotal domains such as power supply fundamentals within technical higher education institutions. This scientific article explores the imperative role of integrating Artificial Intelligence (AI) in teaching the fundamentals of power supply to students in technical higher education institutions. Acknowledging power supply’s fundamental significance and the evolving educational challenges, we advocate for the utilization of AI-powered platforms, simulations, and adaptive learning systems. Through a comprehensive analysis, we highlight the potential benefits and contextualize the implementation of AI in Uzbekistan’s technical education scenario. By embracing AI-driven educational approaches, Uzbekistan can cultivate a skilled workforce adept in power supply, fostering innovation and propelling the nation towards a technologically advanced future.
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.