2025 ASEE Annual Conference & Exposition

Incorporating generative artificial intelligence (AI) into engineering education presents an innovative opportunity to enhance student learning outcomes by equipping future engineers with cutting-edge tools for disaster management and infrastructure resilience. This paper explores how generative AI can be leveraged by engineering educators to teach students advanced techniques for wildfire prediction and geospatial analysis. Focusing on the use of generative AI in the classroom, the methodology demonstrates how students can engage with platforms like Google Earth Engine to access and analyze satellite imagery and environmental datasets, such as MODIS Active Fire Detections and LANDSAT/Sentinel Burn Severity. By integrating generative AI tools (e.g., ChatGPT, Gemini), educators can guide students through the process of automating code generation for wildfire location mapping, enhancing their problem-solving skills and technical competence. The use of generative AI simplifies traditionally complex geospatial analysis tasks, allowing students to focus on interpreting data and understanding the real-world implications of their work. Through hands-on exercises, students can apply AI-driven models to identify wildfire-prone areas, gaining practical experience in disaster risk management. Moreover, the flexibility of generative AI extends to a variety of natural disasters, including floods, hurricanes, and tornadoes, allowing educators to incorporate diverse scenarios into their teaching. This approach not only deepens students' technical knowledge but also fosters critical thinking and prepares them for the evolving challenges in the engineering profession. By leveraging generative AI in the classroom, engineering educators can significantly improve students’ understanding of disaster resilience, proactive planning, and the ethical use of technology in civil engineering contexts.