3
communication patterns between novice and expert drillers
during oil and gas operations. These findings underscore
the importance of targeted training to develop SA.
Naqvi et al. (2020) integrated simulators and eye-track-
ing technology to design training frameworks for oil and
gas kick control, demonstrating the utility of eye-tracking
in enhancing SA. Raza et al. (2023) further explored eye-
tracking in immersive environments, identifying significant
differences in eye movement patterns between novice and
expert operators. These insights offer actionable guidance
for designing training programs that develop SA by focus-
ing on critical cognitive and perceptual skills.
In mining, the application of SA-focused train-
ing, particularly with tools like eye-tracking and simula-
tors, remains underexplored. Integrating these tools into
immersive training programs offers significant potential to
enhance miners’ abilities to perceive, comprehend, and act
effectively in high-pressure situations.
PROPOSED TRAINING FRAMEWORK
The Mine Escape Research, Innovation, and Technology
(MERIT) Center at Missouri S&T is pioneering innovative
strategies to enhance miners’ ability to self-escape during
emergencies in underground environments. Supported by
two CDC-NIOSH U60 project grants, the research focuses
on addressing critical challenges such as underground wire-
less communication, human-robot interaction for miner
self-escape, ingress/egress mechanisms for refuge alternatives
after explosions, evaluating risks associated with lithium-
ion battery electric vehicle fires, and implementing a “train
the trainer” model for disseminating knowledge. Figure 1
presents a research focus areas at the MERIT Center. While
these technological advancements offer promising solu-
tions, their effectiveness hinges on the ability of miners to
use them proficiently, especially in high-stress situations.
This underscores the need for an advanced, adaptive train-
ing framework tailored to the unique challenges of mining
emergencies.
This research is proposing an adaptive approach to
training framework for mining self-escape strategies and
technologies. Kelley (1969) defined the adaptive training as
training where the problem, the stimulus, or the task is var-
ied based on the performance of the trainee. Therefore, for
a training system to be adaptive, it should have three funda-
mental components: adaptive logic, adaptive variable, and
performance assessment (Zahabi &Abdul Razak, 2020).
The proposed Adaptive Immersive Training Framework
(AITF) is conceived to bridge the gap between traditional
training approaches and the need for advanced skills in self-
escape strategies. Grounded in Kelley’s (1969) concept of
adaptive training, the AITF incorporates immersive tech-
nologies and data-driven feedback mechanisms to create a
comprehensive training ecosystem.
Central to the AITF is the integration of immersive
simulation environments, such as virtual reality (VR) and
augmented reality (AR), which replicate real-world mining
emergencies like structural collapses, gas leaks, and fires.
These simulations offer a risk-free environment for min-
ers to practice critical responses, enhancing their situational
awareness and decision-making skills. Realistic scenarios
are tailored to match varying levels of expertise, ensuring
that training is both challenging and relevant. The AITF is
schematically explained in Figure 2.
The AITF establishes a robust learning system
grounded in personalized assessments and iterative train-
ing. It begins with passive testing to evaluate the trainee’s
personality traits, domain-specific knowledge, and prior
experience, providing a tailored baseline for the train-
ing process. Following this, trainees engage in immersive
simulations designed around personality-specific scenarios,
Figure 1. A schematic view of research focus areas at the MERIT Center
communication patterns between novice and expert drillers
during oil and gas operations. These findings underscore
the importance of targeted training to develop SA.
Naqvi et al. (2020) integrated simulators and eye-track-
ing technology to design training frameworks for oil and
gas kick control, demonstrating the utility of eye-tracking
in enhancing SA. Raza et al. (2023) further explored eye-
tracking in immersive environments, identifying significant
differences in eye movement patterns between novice and
expert operators. These insights offer actionable guidance
for designing training programs that develop SA by focus-
ing on critical cognitive and perceptual skills.
In mining, the application of SA-focused train-
ing, particularly with tools like eye-tracking and simula-
tors, remains underexplored. Integrating these tools into
immersive training programs offers significant potential to
enhance miners’ abilities to perceive, comprehend, and act
effectively in high-pressure situations.
PROPOSED TRAINING FRAMEWORK
The Mine Escape Research, Innovation, and Technology
(MERIT) Center at Missouri S&T is pioneering innovative
strategies to enhance miners’ ability to self-escape during
emergencies in underground environments. Supported by
two CDC-NIOSH U60 project grants, the research focuses
on addressing critical challenges such as underground wire-
less communication, human-robot interaction for miner
self-escape, ingress/egress mechanisms for refuge alternatives
after explosions, evaluating risks associated with lithium-
ion battery electric vehicle fires, and implementing a “train
the trainer” model for disseminating knowledge. Figure 1
presents a research focus areas at the MERIT Center. While
these technological advancements offer promising solu-
tions, their effectiveness hinges on the ability of miners to
use them proficiently, especially in high-stress situations.
This underscores the need for an advanced, adaptive train-
ing framework tailored to the unique challenges of mining
emergencies.
This research is proposing an adaptive approach to
training framework for mining self-escape strategies and
technologies. Kelley (1969) defined the adaptive training as
training where the problem, the stimulus, or the task is var-
ied based on the performance of the trainee. Therefore, for
a training system to be adaptive, it should have three funda-
mental components: adaptive logic, adaptive variable, and
performance assessment (Zahabi &Abdul Razak, 2020).
The proposed Adaptive Immersive Training Framework
(AITF) is conceived to bridge the gap between traditional
training approaches and the need for advanced skills in self-
escape strategies. Grounded in Kelley’s (1969) concept of
adaptive training, the AITF incorporates immersive tech-
nologies and data-driven feedback mechanisms to create a
comprehensive training ecosystem.
Central to the AITF is the integration of immersive
simulation environments, such as virtual reality (VR) and
augmented reality (AR), which replicate real-world mining
emergencies like structural collapses, gas leaks, and fires.
These simulations offer a risk-free environment for min-
ers to practice critical responses, enhancing their situational
awareness and decision-making skills. Realistic scenarios
are tailored to match varying levels of expertise, ensuring
that training is both challenging and relevant. The AITF is
schematically explained in Figure 2.
The AITF establishes a robust learning system
grounded in personalized assessments and iterative train-
ing. It begins with passive testing to evaluate the trainee’s
personality traits, domain-specific knowledge, and prior
experience, providing a tailored baseline for the train-
ing process. Following this, trainees engage in immersive
simulations designed around personality-specific scenarios,
Figure 1. A schematic view of research focus areas at the MERIT Center