The assessment of a person's traits is a fundamental prob-lem in human sciences. Compared to traditional paper & pencil tests, computer based assessments not only facilitate data acquisition and processing but also allow for adaptive and personalized tests so that competency levels are assessed with fewer items. We focus on speeded tests and propose a mathematically sound framework in which latent compe-tency skills are represented by belief distributions on com-pact intervals. Our algorithm updates belief based on di-rectional feedback; adaptation rate and difficulty of the task at hand can be controlled by user-defined parameters. We provide a rigorous theoretical analysis of our approach and report on empirical results on simulated and real world data, including concentration tests and the assessment of reading skills.