This paper addresses the problem of generating good example contexts to help children learn vocabulary. We construct candidate contexts from the Google N-gram corpus. We propose a set of constraints on good contexts, and use them to filter candidate example contexts. We evaluate the automatically generated contexts by comparison to example contexts from children’s dictionaries and from children’s stories.