Abstract
Underground mining environments are complex and hazardous operations where emergencies continue to happen. Underground mine emergencies require rapid, high-stakes decision-making under conditions of uncertainty, stress, and limited visibility. Conventional mine emergency training largely relies on instruction-based approaches which provide insufficient exposure to the cognitive and behavioral demands of real underground emergency situations. There has been an identified need to train miners for knowledge, skills, abilities, and other characteristics (KSAOs). This study proposes an Adaptive Immersive Training Framework (AITF), a cognitively grounded architecture that integrates cognitive task analysis (CTA), KSAOs, and situational awareness assessment for miner self-escape training and readiness. The AITF aligns NIOSH-identified self-escape competencies with immersive training scenarios designed to assess and develop cognitive readiness and decision-making. CTA of historical mine accidents is introduced as a foundational design method for translating accident investigation findings into simulation scenarios and performance metrics. A CTA of 2006 Darby Mine No. 1 explosion is presented as a proof of concept. The proposed framework supports individualized assessment, iterative scenario refinement, and data-driven feedback. The AITF advances miner training toward cognitive preparedness during mine emergencies and provides a foundation for future training systems that leverage digital tools, digital twins, and artificial intelligence for the mines of the future.
Recommended Citation
M. A. Raza et al., "An Adaptive Immersive Training Framework for Miner Self-Escape Readiness in Underground Mining Emergencies," Mining, vol. 6, no. 1, article no. 22, MDPI, Mar 2026.
The definitive version is available at https://doi.org/10.3390/mining6010022
Department(s)
Mining Engineering
Publication Status
Open Access
Keywords and Phrases
cognitive task analysis; human factors; immersive training; mine rescue; mine safety; self-escape training; situational awareness; virtual reality
International Standard Serial Number (ISSN)
2673-6489
Document Type
Article - Journal
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2026 The Authors, All rights reserved.
Creative Commons Licensing
This work is licensed under a Creative Commons Attribution 4.0 License.
Publication Date
01 Mar 2026
Comments
Centers for Disease Control and Prevention, Grant None