ITSs have the requirement to be adaptive to the student with AI. The classical ITS architecture defines three components to split the data and to keep it flexible and thus adaptive. However, there is a lack of abstract descriptions how to put adaptive behavior into practice. This paper defines how you can structure your data for case based systems in a way that adaptivity is easier to achieve while maintaining the classical splitting of the system and reducing the data footprint. Building a case based system from a collection of exchangeable steps is also possible with this approach. Two variants of adaptivity based on the data structure are explored and both can be used in conjunction. [For the full proceedings, see ED621108.]