INNOVATIONS
Оригинальная статья
Original paper
Title
DEVELOPMENT OF NETWORK-BASED PROGRAMS FOR ADVANCED TRAINING OF COAL INDUSTRY WORKERS USING INTELLIGENT TRAINING SYSTEMS
Abstract
The article focuses on the development of innovative networkbased programs for advanced training of coal industry workers using intelligent training systems. The purpose of the research is to create conceptual and methodological foundations to create AI-technologies for corporate online learning in order to develop digital competencies of specialists in the industry. This methodology includes analysis of the world's best e-learning practices, design of intelligent educational system architecture, and development of data-driven adaptive learning scenarios. The empirical base includes the survey results of 120 experts, 15 cases of coal companies, and digital footprint arrays of 2,500 trainees. The study helped to identify the key principles of of making online course intelligent, i.e. personalized trajectories (r = 0.86), adaptive progress analytics (r = 0.79), and interactive content (r = 0.74). A modular structure of an AI-based system is proposed, integrated with HR processes and HR analytics. A prototype of the platform has been developed, which showed a 34% increase in the training performance. The scientific significance consists in the development of a methodology for designing intelligent learning ecosystems. The practical effect is associated with the potential for replication of solutions for HR support of digital transformation of the industry.
Keywords
Artificial intelligence, adaptive learning, network-based programs, coal industry, digital competencies, intelligent systems.
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Acknowledgements
This publication was supported by the Strategic Academic Leadership Program of the Peoples' Friendship University of Russia (RUDN).
For citation
Bagmat O.N., Ignashina Z.N., Rybakova I.A. Development of network-based programs for advanced training of coal industry workers using intelligent training systems. Ugol'. 2025;(1):37-44. (In Russ.). DOI: 10.18796/0041-5790-2025-1-37-44.
Paper Info
Received December 4, 2024
Reviewed December 16, 2024
Accepted December 26, 2024
