While simulations are powerful tools for scientific inquiry, most students need scaffolding to engage productively in simulation-based inquiry. This project will develop and study an automated feedback system designed to support middle school students' simulation-based inquiry into wildfires, floods, and hurricanes. The system, called Hazbot, will leverage advanced artificial intelligence (AI) technologies--including machine learning and large language models (LLMs)--to provide timely, personalized feedback as students investigate the three different natural hazards. Hazbot will guide students to collect, analyze, and interpret data from simulations and develop scientific arguments based on that data. Hazbot will also synthesize the automated performance diagnosis and feedback information provided to students and offer teachers targeted instructional suggestions to support individual students and the whole class. The project will research the automated scoring methods, the automated feedback system, the combinations of teacher facilitation and automated feedback needed to support students' simulation-based inquiry, and the impact of Hazbot-integrated wildfire, flood, and hurricane modules on student learning outcomes. The materials generated through design and development will be made available for free to all future students, teachers, and researchers beyond the participants outlined in the project. ISLAND (Intelligent Simulation-based Learning About Natural Disasters)