Mathematical word problems represent a common item format for assessing student competencies. Automatic item generation (AIG) is an effective way of constructing many items with predictable difficulties, based on a set of predefined task parameters. The current study presents a framework for the automatic generation of probability word problems based on templates that allow for the generation of word problems involving different topics from probability theory. It was tested in a pilot study with N = 146 German university students. The items show a good fit to the Rasch model. Item difficulties can be explained by the Linear Logistic Test Model (LLTM) and by the random-effects LLTM. The practical implications of these findings for future test development in the assessment of probability competencies are also discussed. (Contains 2 figures and 2 tables.)