In Madhya Pradesh, cops are using AI to modernise: ‘We need a new type of policing’

The “extended reality” (ER) weapon simulator will help the state police through highly immersive and realistic environments, where personnel can safely develop and refine their tactical skills without the inherent dangers or substantial costs associated with live ammunition exercises and field operations, officials said.

The ER system, specifically designed for Madhya Pradesh, creates fully immersive training environments replicating diverse terrain conditions from dense forest regions where counter-insurgency operations are conducted to urban environments requiring anti-terrorism responses.

The system will generate lifelike scenarios, featuring tribal forest areas, rural settlements, and crowded urban conflict zones, allowing personnel to train in conditions closely mirroring actual deployment territories. “Counter-insurgency missions, ambush response protocols, and hostage rescue operations are embedded within the training architecture, with scenarios developed from real-world operational intelligence and tactical requirements,” said an official.

Explaining the benefits of this system, a senior official said that virtual reality components eliminate traditional training limitations by removing live ammunition requirements while providing unlimited scenario repetition capabilities.

Officials said the system fundamentally “transforms traditional training methodologies by offering a controlled, repeatable environment where trainees can rehearse, refine, and master their responses to high-pressure situations through scenario-based learning that mirrors real-world operational demands”.

The firing range AI system addresses the most technically demanding aspects of weapons training, focusing on marksmanship, weapon handling, and safety protocol adherence, officials said.

According to them, current conventional firing range systems face significant limitations, including heavy dependence on range instructors for posture correction, absence of instantaneous granular feedback during live fire exercises, lack of digital performance logs for tracking improvement, and human error variability in assessment.

The AI component transforms drill nursery training for new recruits by addressing fundamental challenges in the basic instruction of new recruits.

This system tackles key challenges in basic training, including uniform training protocols despite diverse fitness baselines among recruits, overtraining risks and injury management concerns, manual and inconsistent progress tracking methods, and the pressing need for real-time, personalised, adaptive training approaches, officials said.

“AI algorithms adapt routines to individual cadet performance levels and recovery requirements. This enables instructors to focus on coaching and mentorship rather than constant monitoring and correction,” the official said.