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AI and mixed reality can give officers new ways to rehearse procedures and help command staff interpret an operation as it unfolds. Current examples include an AI-and-augmented-reality prototype for operational awareness and virtual- or mixed-reality training studies. They show promising uses and practical challenges, but they do not establish that these tools improve policing outcomes across agencies.
Where AI and mixed reality fit in police work
Mixed reality (MR) blends digital content with a view of the physical environment; augmented reality (AR) overlays digital information on that view, while virtual reality (VR) places a user in a simulated environment. In policing, the examples covered here fall into two broad categories: operational awareness during an incident, and training or investigation practice in a simulation.
Operational awareness: DARLENE
The European Commission’s DARLENE project developed an Augmented Command & Control application intended to improve cooperation between headquarters and officers during an operation. It renders a 3D reconstruction of the operation site and enriches it with real-time computer-vision results, giving headquarters staff a consolidated view. The project also reported a wearable AR helmet and portable computing unit that use AI to analyse the wearer’s field of view.
DARLENE is described as a project prototype, with development, officer feedback, and planned training or piloting—not as a broadly deployed, independently validated police product. Its design used private 5G infrastructure and authentication between computing nodes. The project also described ethics and legal work, an external Ethics Advisory Board, ethics clearances before research with human participants, and efforts to limit data storage through real-time processing. These are project-reported safeguards, not proof that privacy, security, or bias risks have been eliminated.
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Practice without using an active crime scene
An MR crime-scene system studied with trained Kuwaiti police officers reconstructed a scene using a Microsoft HoloLens 2, a FARO X130 point-cloud scanner, and photogrammetry. A spatial reconstruction can provide a place to practise scene procedures without taking trainees into an active crime scene. The study evaluated a demo system, however, rather than a complete practical investigation curriculum.
What police training studies found
Kuwaiti police MR crime-scene study
In a 2024 study published in Virtual Reality, Meshal Albeedan, Hoshang Kolivand, and Ramy Hammady asked 44 young police investigators to evaluate the MR system. The authors reported positive satisfaction and user-experience ratings, while also noting that participants struggled with hand-interaction instructions and learning to use the system.
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In that sample, 75% said they would use the MR system if it could support the entire practical program. That is stated willingness to use a demo system, not evidence of agency adoption or better investigation results. The study also reported that 70.45% of participants had generic knowledge of XR technologies. Neither figure should be generalized to police officers as a whole.
Abu Dhabi Police VR arson simulation
A 2024 article in Policing: A Journal of Policy and Practice examined Abu Dhabi Police’s VR arson crime-scene investigation simulation through qualitative interviews. Participants described engagement, immersion, perceived procedural knowledge and skills, and possible time and cost efficiencies. Those findings reflect interviewees’ perceptions; they are not experimental proof of improved operational performance or cost-effectiveness.
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The authors also identified accessibility and inclusivity limitations, including for people with disabilities, and noted that empirical evidence about VR effectiveness and cost-effectiveness remains limited. A training program therefore needs to consider who can use its equipment and whether the simulated task suits the people expected to train with it.
AI-generated scenarios and the risk of narrow rehearsal
Petros Violakis’s 2025 Oxford Academic article considers large language models (LLMs) for simulation-based law-enforcement training. It discusses adaptive, conversational scenarios and promising study-specific results, while noting that bias and ethical issues remain under investigation. This is not evidence that LLM-based training has been proven to make officers safer or improve field outcomes.
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Gareth Stubbs’s 2025 article in Policing: A Journal of Policy and Practice raises a related scenario-design concern: repeated exposure to similar simulations could habituate trainees to a narrow script, a possibility described as developing “training scars.” Stubbs proposes AI-generated variation as a possible way to reduce that risk. The article is a conceptual investigation drawing on cognitive-science theories and prior research; it does not demonstrate that repeated scenarios cause real-world harm or that AI variation has been tested as a remedy.
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What broader agency use does—and does not—show
The U.S. Government Accountability Office’s 2024 survey offers context about immersive technology use across federal civilian agencies, not police agencies alone. GAO surveyed all 23 agencies defined by the Chief Financial Officers Act: 17 reported immersive-technology activities in fiscal year 2022 or 2023, and 13 reported benefits. Workforce training and public outreach were the benefits agencies cited most often.
GAO reported that the Department of Homeland Security used simulators and other immersive technologies to train nearly 10,000 employees in FY 2022. The training included law-enforcement use-of-force practice, transportation security, and fire and emergency response. This is a measure of training activity, not an independent measure of learning or field performance. Sixteen surveyed agencies planned expansion in FY 2024–2028; 15 planned to adopt or expand use, and 13 planned research and development. Agencies also cited cybersecurity and privacy requirements, along with high operating and maintenance costs, as adoption challenges.
How an agency can judge whether a system is worth piloting
A useful pilot starts with a specific task, not with the headset or AI feature. An agency can compare a proposed system against its existing training or operational process using questions such as these:
- Task and audience: Is the system intended for use-of-force practice, de-escalation, crime-scene procedure, arson investigation, emergency response, or command awareness? Who needs to use it, and what job should it support?
- Evidence of value: Does the evaluation measure user acceptance, learning, retention, skill transfer, or field outcomes? A positive user-experience rating answers a different question from whether a skill transfers to real work.
- Scenario design: Are scenarios realistic and varied enough to avoid rehearsing one predictable response? If AI generates variations, how will the agency check their accuracy, fairness, and suitability?
- Data governance: Where are video and sensor data processed, transmitted, and retained? Who can access them, and what privacy and cybersecurity reviews are required?
- Human factors: Can users with different abilities and levels of technical familiarity operate the system? Consider accessibility, headset comfort, hand controls, and the time needed to learn the interface.
- Total operating burden: Include hardware, scene capture, content creation and refresh, maintenance, cybersecurity work, facilities, and instructor time—not just the initial equipment.
- Deployment maturity: Establish whether the system is a prototype, a controlled pilot, an agency-developed training system, or an operational deployment. Those stages are not interchangeable.
The cited examples use different populations and study methods, and they do not provide a common outcome measure or head-to-head cost comparison. That makes a universal product ranking—or a general claim that immersive systems outperform traditional training—unsupported by this evidence. A measured pilot tied to a defined task is a more defensible basis for an agency decision.
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