As the popularity of rich assessment scenarios increases so must the availability of psychometric models capable of handling the resulting data. Dynamic Bayesian networks (DBNs) offer a fast, flexible option for characterizing student ability across time under psychometrically complex conditions. In this article, a brief introduction to DBNs is offered, followed by a review of the existing literature on the use of DBNs in educational and psychological measurement with a focus on methodological investigations and novel applications that may provide guidance for practitioners wishing to deploy these models. The article concludes with a discussion of future directions for research in the field.