Knowledge tracing is a fundamental area of educational data modeling that aims at gaining a better understanding of the learning occurring in tutoring systems. Knowledge tracing models fit various parameters on observed student performance and are evaluated through several goodness of fit metrics. Fitted parameter values are of crucial interest in order to diagnose learning mastery as well as knowledge models and qualitative aspects of the learning environment. Unfortunately, parameter values are rarely associated with standard errors or confidence intervals, both of which are critical information to validate the inferences that can be made from the model. Taking the example of the Additive Factor Model, we describe how to obtain standard errors on the model parameters. We propose two methods to compute those and discuss results obtained on a public dataset. [For the full proceedings, see ED593090.]