We present an intelligent tutoring system that lets students of Chinese learn words and grammatical constructions. It relies on a Bayesian, linguistically motivated cognitive model that represents the learner's knowledge. This model is dynamically updated given observations about the learner's behaviour in the exercises, and employed at runtime to select the exercises that are expected to maximise the learning outcome. Compared with a baseline that randomly chooses exercises at user's declared level, the system shows positive effects on users' assessment of how much they have learnt, which suggests that it leads to enhanced learning. [For full proceedings, see ED565087.]