Utilizing Large Language Models in Tribal Emergency Management
Srishti Gupta, Yu‐Che Chen, Chun-Hua Tsai
Abstract
This paper explores the unique challenges faced by tribal communities in the context of emergency management, encompassing natural disasters and the preservation of their rich cultural heritage. The study aims to investigate both the potential advantages and hurdles associated with the adoption of large language models (LLMs) in tribal emergency management. Our primary goal is to qualitatively assess Indigenous perspectives on the suitability and acceptability of deploying an LLM-powered chatbot in this specific domain. To achieve this objective, we employ a think-aloud interview methodology involving 18 tribal members. This qualitative research approach captures participants’ cognitive processes and decision-making as they engage with the language model’s responses in real-time. Through thematic analysis of these verbalized thoughts and the prompts submitted, the study sheds light on various aspects, including usability, information-seeking behavior, and the incorporation of tribal culture considerations when integrating large language models into tribal emergency management practices. The paper concludes with a discussion of potential design implications and contributions to the fields of AI and HCI.