The difficulty of domain knowledge acquisition is one of the most sensible challenges of intelligent tutoring systems. Relying on domain experts and building domain models from scratch are not viable solutions. The ability to automatically extract domain knowledge from documents can contribute to overcome these difficulties. In this paper, we use machine learning and natural language processing to parse documents and to generate domain concept maps and ontologies. We also show how an intelligent tutoring system benefits from the generated structures.