In a typical citizen science/crowdsourcing environment, the contributors label items. When there are few labels, it is straightforward to train contributors and judge the quality of their labels by giving a few examples with known answers. Neither is true when there are thousands of domain-specic labels and annotators with heterogeneous skills. This demo paper presents an Active User Training framework implemented as a serious game called The- PlantGame. It is based on a set of data-driven algorithms allowing to (i) actively train annotators, and (ii) evaluate the quality of contributors’ answers on new test items to optimize predictions.