Most current serious games suffer from system inflexibility, which is responsible for stereotyped training scenarios and predictable game-play. My goal is to support adaptivity in serious games by allowing virtual scenarios to dynamically adjust to what and how players should learn. Adaptivity should give players more effective gaming and learning experiences, if it is guided by their Instructors' knowledge about both players and learning goals. I expect to enable Instructors to create adaptive

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... scenarios through specification of: what players have to learn, how they should learn it and, afterwards, what went missing. This work focuses on embedding this sort of knowledge into objects of the virtual scenario, through new semantic modeling methods. Meaningful information on what to learn will be solved into generating pre-game scenarios. How to learn knowledge will be used to create in-game adaptable objects and events. Assessments on what went missing will be solved to re-generate post-game (try again) scenarios.