Leveraging large language models for enhanced simulation-based learning in police and law enforcement

Abstract In this study, the application of Large Language Models (LLMs) in simulation-based training of law enforcement officers is being assessed. Adaptability, real-time response capabilities and dynamic and personalised learning experiences, which closely simulate real-life policing scenarios, are the hallmarks of LLMs. LLMs adjust scenarios according to trainee input to enhance learning engagement, facilitate better decision-making, and improve skill retention. Finally, the study shows how LLMs contribute to realism in training, especially in high-stakes situations such as crisis negotiation and suspect interrogation. That being said, bias and ethical concerns are currently being investigated in relation to the application of large language models (LLMs). In this study, LLM-driven simulations are assessed using a mixed-methods approach that blends qualitative feedback with quantitative data. Results suggest that LLMs significantly enhance trainee preparedness for unpredictable real-world encounters and thus can present a scalable and low-cost training solution for law enforcement training.