An Adaptive Immersive Training Framework for Miner Self-Escape Readiness in Underground Mining Emergencies

Underground mining environments are complex and hazardous operations where emergencies continue to happen. Post-incident investigations consistently identify training gaps in human related factors such as situational awareness and decision-making under stress. 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) for miner self-escape readiness integrating immersive technology, situational awareness theory, KSAOs, and cognitive task analysis (CTA). 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. 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.