OceanAI: A Conversational Platform for Accurate, Transparent, Near-Real-Time Oceanographic Insights
Abstract
Artificial intelligence is transforming the sciences, yet general conversational AI systems often generate unverified "hallucinations" undermining scientific rigor. We present OceanAI, a conversational platform that integrates the natural-language fluency of open-source large language models (LLMs) with real-time, parameterized access to authoritative oceanographic data streams hosted by the National Oceanic and Atmospheric Administration (NOAA). Each query such as "What was Boston Harbor's highest water level in 2024?" triggers real-time API calls that identify, parse, and synthesize relevant datasets into reproducible natural-language responses and data visualizations. In a blind comparison with three widely used AI chat-interface products, only OceanAI produced NOAA-sourced values with original data references; others either declined to answer or provided unsupported results. Designed for extensibility, OceanAI connects to multiple NOAA data products and variables, supporting applications in marine hazard forecasting, ecosystem assessment, and water-quality monitoring. By grounding outputs and verifiable observations, OceanAI advances transparency, reproducibility, and trust, offering a scalable framework for AI-enabled decision support within the oceans. A public demonstration is available at https://oceanai.ai4ocean.xyz.
Keywords
Cite
@article{arxiv.2511.01019,
title = {OceanAI: A Conversational Platform for Accurate, Transparent, Near-Real-Time Oceanographic Insights},
author = {Bowen Chen and Jayesh Gajbhar and Gregory Dusek and Rob Redmon and Patrick Hogan and Paul Liu and DelWayne Bohnenstiehl and Dongkuan Xu and Ruoying He},
journal= {arXiv preprint arXiv:2511.01019},
year = {2025}
}
Comments
A related presentation will be given at the AGU(American Geophysical Union) and AMS(American Meteorological Society) Annual Meetings