Artificial intelligence (AI) and machine learning (ML) are transforming Earth and environmental science by expanding capabilities in data analysis, modeling, prediction, and decision-relevant applications across complex Earth systems. These advances are also reshaping education and workforce preparation by increasing the need for training in reproducibility, data stewardship, interoperable data practices, and transparent AI-enabled workflows.

This session welcomes contributions on the use of AI/ML in Earth and environmental science research and in the educational pathways that support this work. Topics include, but are not limited to, remote sensing, weather and air quality, climate and hydrologic systems, wildfire and natural hazards, cryosphere science, and Earth system modeling. Contributions addressing curriculum development, experiential learning, field-integrated training, high-performance computing, cyberinfrastructure, and open-science practices are encouraged.

The session will emphasize scientific results, emerging applications, and the educational structures needed to prepare a future workforce capable of using AI/ML responsibly and effectively in Earth system science.

• A - Atmospheric Sciences
• C - Cryosphere
• H - Hydrology
• NH - Natural Hazards