4
where their performance is assessed against established
benchmarks of optimal behavior. During the simulations,
key indicators such as eye-tracking, process tracing, and
physiological responses are monitored to provide insights
into cognitive focus, decision-making, and stress man-
agement. This data is analyzed to generate feedback that
identifies areas for improvement and informs targeted
interventions. Subsequent rounds of training are custom-
ized with updated scenarios and tasks, progressively refin-
ing the trainee’s skills through continuous iteration until
the desired performance standards are met. Furthermore,
the assessment of personality traits aids in job matching,
allowing trainees to be positioned in roles within the min-
ing operation where they are likely to excel, optimizing
both individual and organizational outcomes.
A key innovation in the framework is the incorporation
of eye-tracking technology, which monitor visual atten-
tion and cognitive focus during simulations. By captur-
ing data on how trainees interact with their environment,
eye-tracking provides insights into situational awareness
and highlights differences in performance between novice
and expert miners. Further, the framework integrates other
process tracing tools, such as verbal content, question-
naire, surveys, etc. to gain further insights into the cogni-
tive understanding of the trainee. This data is then used to
deliver targeted interventions, helping trainees refine their
skills in areas where they struggle.
Performance assessment is another crucial component
of the AITF, employing real-time metrics to evaluate a
trainee’s progress. Adaptive logic adjusts training scenarios
dynamically based on these assessments, ensuring that tasks
remain appropriately challenging while addressing specific
learning needs. This adaptive approach promotes con-
tinuous improvement and maintains trainee engagement
throughout the program.
Training outcomes are tied to specific competencies,
such as reaction times, decision accuracy, and adherence
to safety protocols. These milestones not only measure the
trainee’s readiness to handle real-world emergencies but also
provide a clear pathway for skill development. By focusing
on competency acquisition, the framework ensures that
trainees are equipped with the practical skills necessary for
effective self-escape.
Simulation and Scenario Design
Scenarios will simulate common but challenging emergency
situations in underground mining, including blocked pas-
sages, low visibility, and equipment malfunctions. These
scenarios will allow miners to experience the complexities
of emergency decision-making and self-escape without the
associated risks. The simulations are tailored to replicate
conditions where miners must navigate communication
breakdowns and rely on both procedural knowledge and
cognitive adaptability, enhancing their preparedness for
real-world conditions.
EXPECTED RESULTS
The proposed training infrastructure is expected to result
in significant improvements in situational awareness and
decision-making skills. By repeatedly exposing trainees
to high-stress scenarios, the infrastructure should accel-
erate their ability to respond effectively in emergencies.
Additionally, by providing rescuers with the skills needed to
operate Missouri S&T’s new escape technologies, the train-
ing infrastructure will enhance overall rescue efficiency.
The simulators can help with job matching where people
with more refined and measured skills can be positioned
for more suitable jobs. For example, people with more
actively refined motor skills may be better performers on
TRAINEE
Passive Testing
1. Personality
Traits
2. Domain
Knowledge
3. Experience
SIMULATOR
1. Eye-Tracking
2. Process
Tracing
3. Physiological
Indicators
PERFORMANCE
ASSESSMENT
FEEDBACK
/INTERVENTION
DESIGN
TRAINING
SCENARIO
DESIGN
TASK AND
TECHNOL
OGY
Expected
Behavior
Physiological
Monitoring
Ranking of
Trainee
Figure 2. Proposed Training Framework for Simulation-
Based Training of Miners for Self-Escape
where their performance is assessed against established
benchmarks of optimal behavior. During the simulations,
key indicators such as eye-tracking, process tracing, and
physiological responses are monitored to provide insights
into cognitive focus, decision-making, and stress man-
agement. This data is analyzed to generate feedback that
identifies areas for improvement and informs targeted
interventions. Subsequent rounds of training are custom-
ized with updated scenarios and tasks, progressively refin-
ing the trainee’s skills through continuous iteration until
the desired performance standards are met. Furthermore,
the assessment of personality traits aids in job matching,
allowing trainees to be positioned in roles within the min-
ing operation where they are likely to excel, optimizing
both individual and organizational outcomes.
A key innovation in the framework is the incorporation
of eye-tracking technology, which monitor visual atten-
tion and cognitive focus during simulations. By captur-
ing data on how trainees interact with their environment,
eye-tracking provides insights into situational awareness
and highlights differences in performance between novice
and expert miners. Further, the framework integrates other
process tracing tools, such as verbal content, question-
naire, surveys, etc. to gain further insights into the cogni-
tive understanding of the trainee. This data is then used to
deliver targeted interventions, helping trainees refine their
skills in areas where they struggle.
Performance assessment is another crucial component
of the AITF, employing real-time metrics to evaluate a
trainee’s progress. Adaptive logic adjusts training scenarios
dynamically based on these assessments, ensuring that tasks
remain appropriately challenging while addressing specific
learning needs. This adaptive approach promotes con-
tinuous improvement and maintains trainee engagement
throughout the program.
Training outcomes are tied to specific competencies,
such as reaction times, decision accuracy, and adherence
to safety protocols. These milestones not only measure the
trainee’s readiness to handle real-world emergencies but also
provide a clear pathway for skill development. By focusing
on competency acquisition, the framework ensures that
trainees are equipped with the practical skills necessary for
effective self-escape.
Simulation and Scenario Design
Scenarios will simulate common but challenging emergency
situations in underground mining, including blocked pas-
sages, low visibility, and equipment malfunctions. These
scenarios will allow miners to experience the complexities
of emergency decision-making and self-escape without the
associated risks. The simulations are tailored to replicate
conditions where miners must navigate communication
breakdowns and rely on both procedural knowledge and
cognitive adaptability, enhancing their preparedness for
real-world conditions.
EXPECTED RESULTS
The proposed training infrastructure is expected to result
in significant improvements in situational awareness and
decision-making skills. By repeatedly exposing trainees
to high-stress scenarios, the infrastructure should accel-
erate their ability to respond effectively in emergencies.
Additionally, by providing rescuers with the skills needed to
operate Missouri S&T’s new escape technologies, the train-
ing infrastructure will enhance overall rescue efficiency.
The simulators can help with job matching where people
with more refined and measured skills can be positioned
for more suitable jobs. For example, people with more
actively refined motor skills may be better performers on
TRAINEE
Passive Testing
1. Personality
Traits
2. Domain
Knowledge
3. Experience
SIMULATOR
1. Eye-Tracking
2. Process
Tracing
3. Physiological
Indicators
PERFORMANCE
ASSESSMENT
FEEDBACK
/INTERVENTION
DESIGN
TRAINING
SCENARIO
DESIGN
TASK AND
TECHNOL
OGY
Expected
Behavior
Physiological
Monitoring
Ranking of
Trainee
Figure 2. Proposed Training Framework for Simulation-
Based Training of Miners for Self-Escape