A Cognitive Engineering Approach to Capture the Context of Decision Making for Emergency Call Handlers

Abstract

Effective decision making is critical for policing, frequently involving high-stakes situations with meaningful consequences for the public, and spanning a wide range of contexts — from officers attending incidents to analysts processing complex data. Whilst it is well recognised that there is a need for sufficient h uman oversight a nd accountability when using AI technologies in policing, there is a deeper challenge in designing decision support that genuinely enhances rather than undermines expert performance. Policing decisions often involve experts in their domain whose tacit knowledge, contextual reasoning, and accumulated experience are central to the quality of the decisions they make. When technology is designed primarily to reduce friction and offload cognitive effort, it risks eliminating the very thinking processes that produce good outcomes — sometimes manual effort carries significant cognitive value, and in these situations indiscriminate automation may erode expert performance rather than improve it. This paper demonstrates an applied example of a Cognitive Engineering approach for designing decision support that complements human cognition, through three interconnected steps: first, understanding and modelling the nature of user expertise and cognitive work by mapping workflows; s econd, d iscovering t he relationships between cognitive activity and overall performance, including where cognitive load represents meaningful value versus unnecessary burden; and third, translating these insights into potential interjection points for AI technology. Taken together, these steps suggest a different way to run the ‘discovery’ period for AI-enabled decision support: rather than starting from candidate technologies or automation opportunities, discovery begins with characterising expertise in context, then testing which cognitive demands are performance-critical versus merely effortful, and only then forming hypotheses about where AI should interject to amplify expert judgement without eroding it. This methodology was applied to emergency call handling within a policing context, and the findings help challenge assumptions about the potential impact of technological intervention, illustrate the importance of understanding expert cognition before deploying technology, and offer a replicable and applied approach for ensuring AI amplifies rather than replaces human decision-making capability.

Keywords: Cognitive engineering, Decision support, Policing, Artificial intelligence, Call handling, Cognitive task analysis, Human factors

DOI: 10.54941/ahfe1007509