Abstract
TERENCE is an FP7 ICT European project that is developing an adaptive learning system for supporting poor comprehenders and their educators. Its learning material are books of stories and games. The so-called smart games serve to stimulate story comprehension. This paper focuses on the analysis of flat stories with a specific annotation language and the generation of smart games from the analysed texts, mixing natural language processing and temporal constraint-reasoning technologies. The paper ends commenting on the approach to the automated analysis and extraction of information from stories for specific users and domains, briefly evaluating the benefits of the semi-automated generation process in terms of production costs.