Building Holistic Situation Awareness through Large Language Models

10 pages•Published: July 12, 2024

Abstract

In order to design a system to support a users’ situation awareness as they navigate and execute in a complex domain, the information landscape for the domain must be defined. Such a landscape becomes critical for recognizing and evaluating deficiencies in a user’s cognitive processing of the information landscape, and as such, the user’s state of situation awareness. This paper leverages previous work in defining the information landscape for the domain of rotorcraft pilotage, and explores an approach to identifying common deficiencies in a user’s awareness of the information landscape. This paper takes into account prominent frameworks on situation awareness, and presents a methodology leveraging Large Language Models (LLMs) to identify common information deficiencies occurring at various phases of rotorcraft flight through text mining aviation incident and accident reports. Once these deficiencies are identified, the system can provide holistic situation awareness considering a larger data space than the user, and also provide human-centered awareness of prioritized concerns of situation awareness factors and possible mitigations.

Keyphrases: information deficiency, large language models, situation awareness

In: Kenneth Baclawski, Michael Kozak, Kirstie Bellman, Giuseppe D'Aniello, Alicia Ruvinsky and Candida Da Silva Ferreira Barreto (editors). Proceedings of Conference on Cognitive and Computational Aspects of Situation Management 2023, vol 102, pages 189--198

Links:	https://easychair.org/publications/paper/244Pv
	https://doi.org/10.29007/bb8h