Human-AI Use Patterns for Decision-Making in Disaster Scenarios: A Systematic Review

• URL: http://arxiv.org/abs/2509.12034v1
• Date: Mon, 15 Sep 2025 15:18:49 GMT
• Title: Human-AI Use Patterns for Decision-Making in Disaster Scenarios: A Systematic Review
• Authors: Emmanuel Adjei Domfeh, Christopher L. Dancy,
• Abstract summary: We identify four major categories: Human-AI Decision Support Systems, Task and Resource Coordination, Trust and Transparency, and Simulation and Training.<n>Within these, we analyze sub-patterns such as cognitive-augmented intelligence, multi-agent coordination, explainable AI, and virtual training environments.<n>Our review highlights how AI systems may enhance situational awareness, improve response efficiency, and support complex decision-making, while also surfacing critical limitations in scalability, interpretability, and system interoperability.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In high-stakes disaster scenarios, timely and informed decision-making is critical yet often challenged by uncertainty, dynamic environments, and limited resources. This paper presents a systematic review of Human-AI collaboration patterns that support decision-making across all disaster management phases. Drawing from 51 peer-reviewed studies, we identify four major categories: Human-AI Decision Support Systems, Task and Resource Coordination, Trust and Transparency, and Simulation and Training. Within these, we analyze sub-patterns such as cognitive-augmented intelligence, multi-agent coordination, explainable AI, and virtual training environments. Our review highlights how AI systems may enhance situational awareness, improves response efficiency, and support complex decision-making, while also surfacing critical limitations in scalability, interpretability, and system interoperability. We conclude by outlining key challenges and future research directions, emphasizing the need for adaptive, trustworthy, and context-aware Human-AI systems to improve disaster resilience and equitable recovery outcomes.

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