BEHAVIORAL VR TRAINING FOR LAW ENFORCEMENT
Designing safer, data-driven training for high-stakes decision making
Project description
Problem Statement
Partnered on designing a behavioral VR training system aimed at improving preparedness and decision-making through immersive simulations and performance analytics.
From explorations to final designs in 15 weeks while working with multiple projects at the same time
Background
Timeline
Affinity Mapping
Interview Insights
Information Architecture
The initiative focuses on creating scalable training experiences that enable professionals to practice complex scenarios and receive actionable feedback.
Process
Research & Discovery
Design & Prototyping
System Design & Collaboration
Testing & Optimization
This section outlines the structured, human-centered approach used to design a behavioral VR training system, from understanding real-world challenges to developing solutions that support effective learning and performance evaluation.
The resulting training platform enhances how law enforcement agencies prepare officers by combining immersive simulations with structured feedback. The system supports realistic practice, instructor oversight, and continuous learning, helping officers build confidence and make safer decisions in high-pressure situations.
Debriefing Matters
Observers often take notes during training sessions, underscoring the importance of a relaxed atmosphere during debriefing. Participants felt it was crucial to learn from mistakes and understand the consequences of their actions.
Feedback
is Essential training feedback begins at the start of debriefings and includes both verbal and written input, which is crucial for continuous improvement.
Playback Functionality
a notable gap in current VR training is the lack of a playback feature. Trainees expressed a strong interest in being able to review their performance, compare it to peers, and set goals for improvement.
"How might we leverage data from VR training equipment to develop a Tracking & Evaluation system that enhances training and unlocks the full potential of these VR systems?"
Conducted exploratory research to understand the complexities of training in high-pressure environments and the challenges faced by trainers and trainees. Reviewed existing VR training tools, studied decision-making under stress, and gathered insights through interviews and secondary research to identify gaps in current training methods.
Developed wireframes and interactive prototypes to explore training scenarios, trainer dashboards, and performance review workflows. Iterated on designs to reduce cognitive load and ensure critical information was presented clearly during high-stress moments. Focused on creating intuitive interfaces that balance immersive experiences with practical usability for trainers monitoring sessions.
Worked closely with stakeholders to align on feature priorities and system capabilities. Defined behavioral metrics such as reaction time, decision pathways, and scenario outcomes to support meaningful performance evaluation. Ensured the design could scale across training programs while maintaining flexibility for different scenarios.
Interactive simulations allow officers to engage with realistic situations such as conflict resolution, crisis response, and community interactions, enabling hands-on learning in a controlled environment.
Interactive simulations allow officers to engage with realistic situations such as conflict resolution, crisis response, and community interactions, enabling hands-on learning in a controlled environment.
Instructors can monitor sessions as they unfold, observing decision points, communication patterns, and responses, allowing immediate guidance and more effective coaching.
Post-training analytics highlight performance trends, helping officers reflect on actions, understand outcomes, and identify opportunities for improvement.
Conducted concept testing and feedback sessions to evaluate usability and clarity of interactions. Refined workflows based on feedback to improve navigation, information hierarchy, and overall user experience, and validated that the system supported both real-time monitoring and post-training reflection effectively.
Solution
Scenario-Drive Training
Real-Time Instructor Visibility
Behavioral Insights & Feedback
Training scenarios can be customized based on skill level, training goals, or department needs, supporting personalized learning paths.
Adaptive Learning Experience
The platform encourages iterative learning by enabling repeat practice, progress tracking, and data-driven adjustments to training programs.
Continuous Improvement Loop
DASHBOARD
Easily plan and manage training sessions across departments, scenarios, and groups to ensure structured and consistent training delivery.
AFTER ACTION REPORT
Detailed behavioral insights (movement, decisions, actions) help instructors understand how trainees respond under pressure and identify areas for improvement.
SCENARIO
A centralized library where trainers can browse, filter, and manage training scenarios — enabling quick access to diverse real-world simulations tailored for law enforcement training.
SESSION PLAYBACK & VIDEO RECORDINGS
Instructors can add comments directly within the playback experience, linking feedback to specific moments to provide actionable guidance for improvement.
AFTER ACTION REPORT
Detailed behavioral insights (movement, decisions, actions) help instructors understand how trainees respond under pressure and identify areas for improvement.
This project introduced a structured, data-driven approach to evaluating VR training, enabling trainers and departments to move beyond observation-based assessments toward measurable performance insights.
Trainers gained a centralized view of session data, behavioral metrics, and physiological signals, allowing them to better understand trainee performance and identify areas for improvement across sessions.
The introduction of playback tools and structured reports streamlined post-training discussions, helping instructors provide targeted feedback and reinforce learning immediately after simulations.
By surfacing actionable insights, such as behavioral patterns, stress indicators, and scenario outcomes, the system supported more informed evaluations of trainee readiness in high-pressure situations.
Results
Improved Training Visibility
Faster, More Effective Debriefs
Increased Decision Confidence
Increased Decision Confidence
The platform closed the gap between simulation and reflection by enabling trainees to review sessions, learn from mistakes, and track progress over time, fostering continuous improvement.
Enhanced Learning Loop