Paper Type
Complete
Abstract
The increasing frequency and severity of disasters have increasingly overwhelmed traditional response capabilities, necessitating a shift from passive tools to autonomous agentic systems. Whilst current AI excels at specialized tasks, a research gap remains in integrating these capabilities into a cohesive information system (IS) capable of independent reasoning and action, designed for intelligent human-AI collaboration. This paper proposes a novel conceptual architecture for a dedicated Environmental Question-Answering System (ENV-QAS), a hierarchical Agentic AI framework unifying a Multi-modal Large Language Model (MLLM), Temporal Knowledge Graphs (TKG), and Retrieval-Augmented Generation (RAG). Grounding our framework in the Uses and Gratifications Theory (UGT), ENV-QAS aims to enable real-time disaster decision-making and enhance human-AI collaboration in the field through accessible decision support. By bridging data gaps and enabling multi-agent collaboration within a centralized reasoning engine, this study contributes to the next generation of IS artifacts. As a design-science artifact, ENV-QAS is accompanied by a concrete evaluation plan positioning it for empirical validation in future work.
Paper Number
1672
Recommended Citation
Thorat, Supriya; Huangfu, Luwen; Bery, Aadi; and Morita, Keigo, "Agentic AI for Crisis Informatics: A Multi-Agent Framework for Scalable and Reliable Disaster Communication" (2026). AMCIS 2026 Proceedings. 6.
https://aisel.aisnet.org/amcis2026/sig_odis/sig_odis/6
Agentic AI for Crisis Informatics: A Multi-Agent Framework for Scalable and Reliable Disaster Communication
The increasing frequency and severity of disasters have increasingly overwhelmed traditional response capabilities, necessitating a shift from passive tools to autonomous agentic systems. Whilst current AI excels at specialized tasks, a research gap remains in integrating these capabilities into a cohesive information system (IS) capable of independent reasoning and action, designed for intelligent human-AI collaboration. This paper proposes a novel conceptual architecture for a dedicated Environmental Question-Answering System (ENV-QAS), a hierarchical Agentic AI framework unifying a Multi-modal Large Language Model (MLLM), Temporal Knowledge Graphs (TKG), and Retrieval-Augmented Generation (RAG). Grounding our framework in the Uses and Gratifications Theory (UGT), ENV-QAS aims to enable real-time disaster decision-making and enhance human-AI collaboration in the field through accessible decision support. By bridging data gaps and enabling multi-agent collaboration within a centralized reasoning engine, this study contributes to the next generation of IS artifacts. As a design-science artifact, ENV-QAS is accompanied by a concrete evaluation plan positioning it for empirical validation in future work.
When commenting on articles, please be friendly, welcoming, respectful and abide by the AIS eLibrary Discussion Thread Code of Conduct posted here.
Comments
SIGODIS