This project brings spatially explicit species distribution modeling (SOM) using Maxent (Maximum Entropy Modeling) into the classroom. Designed for advanced graduate-level spatial analysis courses, it integrates machine learning principles, spatial data manipulation, and model interpretation to teach students how environmental variables influence spatial distributions of particular species. Using real-world crop data from Hawaii, students combine georeferenced occurrence points with environmental and soil variables to predict suitable cultivation areas. The curriculum covers data preparation, model configuration, execution, and validation, emphasizing machine learning concepts and spatial interpretability. Students learn to adjust Maxent parameters, interpret ROC curves and AUC scores, and use jackknife tests and response curves to assess variable importance. The outputs can readily be visualized with GIS to enhance communication and spatial storytelling.