Building an Intelligent Data Exploring Assistant for Geoscientists

Abstract Advances in natural‐language processing and large language models (LLMs) are transforming how geoscientists interact with complex data sets, enabling efficient and intuitive scientific analyses. This study introduces the Intelligent Data Exploring Assistant (IDEA), a prototype software framework that integrates existing LLM technology with domain‐specific instructions, data, analytical tools, and computing resources to support geoscientific research. We demonstrate its application through the Station Explorer Assistant (SEA), a web‐based tool designed for sea level scientists. SEA empowers users to analyze and interpret coastal water level data by addressing challenges such as vertical datum conversions and assessing flooding risks. We also demonstrate the generalizability of building an IDEA, whereby we deploy a local instance of the framework to analyze atmospheric observations from Mars collected by NASA's InSight Mission. By combining LLM capabilities with robust domain‐specific customizations, SEA and the Mars IDEA generate accurate analyses, visualizations, and insights through natural‐language prompts. This study highlights the potential of IDEA frameworks to lower technical barriers, enhance educational opportunities, and transform geoscientific workflows while addressing the limitations and uncertainties of current LLM technology.