GeoAI is transforming how Earth observation data are analyzed and interpreted, yet its integration into geospatial education is often constrained by limited accessibility, reproducibility, and computing resources. This project addresses these challenges by developing open-source, cloud-enabled teaching modules that support end-to-end GeoAI workflows for spatial analysis. The modules leverage Python-based tools such as SamGeo and GeoAI to guide learners through key stages of the GeoAI pipeline, including cloud-based data access, model development, and image segmentation using deep learning techniques. Built on interactive Jupyter Notebooks and deployed via I-GUIDE JupyterHub, the materials ensure a consistent, scalable, and reproducible learning environment.