Intelligent tutoring systems are adapting the curriculum to the needs of the student. The integration of stealth assessments of student traits into tutoring systems, i.e. the automatic detection of student characteristics has the potential to re?ne this adaptation. We present a pipeline for integrating automatic assessment seamlessly into a tutoring system and apply the method to the case of developmental dyscalculia (DD). The proposed classi?er is based on user inputs only, allowing non-intrusive and unsupervised, universal screening of children. We demonstrate that interaction logs provide enough information to identify children at risk of DD with high accuracy and validity and reliability comparable to traditional assessments. Our model is able to adapt the duration of the screening test to the individual child and can classify a child at risk of DD with an accuracy of 91% after 11 minutes on average.