  Synthetic data generation has been a growing area of research in recent
years. However, its potential applications in serious games have not been
thoroughly explored. Advances in this field could anticipate data modelling and
analysis, as well as speed up the development process. To try to fill this gap
in the literature, we propose a simulator architecture for generating
probabilistic synthetic data for serious games based on interactive narratives.
This architecture is designed to be generic and modular so that it can be used
by other researchers on similar problems. To simulate the interaction of
synthetic players with questions, we use a cognitive testing model based on the
Item Response Theory framework. We also show how probabilistic graphical models
(in particular Bayesian networks) can be used to introduce expert knowledge and
external data into the simulation. Finally, we apply the proposed architecture
and methods in a use case of a serious game focused on cyberbullying. We
perform Bayesian inference experiments using a hierarchical model to
demonstrate the identifiability and robustness of the generated data.
