An Assistive System for Transferring Domain Knowledge to Novice Officers

Abstract

Instructional strategies in many operative fields, including law enforcement, have reached a high level of complexity due to dynamically changing task environments and the introduction of different technologies to help the users in their operational work. In the last decades, a transition has been observed from dedicated trainers to the adoption of automated technologies to support the trainees. Based on a review of state-of-the-art literature and direct feedback from law enforcement agencies, we have developed an assistive system to aid in the knowledge transfer from expert to novice officers, and, consequently, improve the time necessary to train novel users. This system is grounded on the most relevant instructional principles derived from cognitive and learning theories. The result is a system that can dynamically deliver suggestions based on previous successful actions from other users and the current performance and state of the user. To validate it, we implemented a knowledge graph exploration task, in which users can receive suggestions from the system. Here, we propose this novel knowledge transfer system by presenting the corresponding literature review together with the conceptualization of the architecture of the assistive system and the implementation of a validation task. With this work, we aim at facilitating the transfer of domain knowledge, which could have a significant impact on the training and education of law enforcement officials in and for the Digital Age.