ORIGINAL RESEARCH article
Front. Ecol. Evol.
Sec. Conservation and Restoration Ecology
This article is part of the Research TopicCoastal Adaptation Through Nature: Natural and Nature-Based Features (NNBF) ResearchView all 14 articles
AI-Driven Insights on Coastal Management: A Comparative Discussion of the use of Generative Chatbots and Natural Infrastructure
Provisionally accepted
- Coastal and Hydraulics Laboratory, Engineer Research and Development Center (ERDC), Vicksburg, United States
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As technology rapidly advances, so do our computational abilities and access to vast datasets. The emergence of cloud computing platforms has catalyzed the development of numerous generative artificial intelligence (AI) chatbots, offering users unprecedented access to information and reasoning capabilities at their fingertips, provided they have internet access. With the increasing integration of generative AI technologies into daily life, critical questions arise about their ability to apply nuanced reasoning and evaluate complex issues effectively at both local and global scales. This manuscript explores AI's current perspectives on coastal management, focusing particularly on the implementation of natural infrastructure. By examining the insights provided by three widely used generative AI chatbots, the authors present a comparative analysis of AI-generated opinions on coastal management. This analysis employs a scored system that evaluates accuracy, completeness, relevance, clarity, and depth. The findings underscore the potential and limitations of these tools to inform and advance the field of natural infrastructure through fostering better understanding, supporting decision-making, and promoting the adoption of sustainable, nature-based solutions for coastal resilience.
Keywords: coastal management, coastalresilience, Decision Support, Generative AI, natural infrastructure
Received: 27 May 2025; Accepted: 29 Jan 2026.
Copyright: © 2026 Dillon, Tritinger and Provost. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Catie Dillon
Amanda Tritinger
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