The assessment of a person’s traits such as ability is a fundamental problem in human sciences. We focus on assessments of traits that can be measured
by determining the shortest time limit allowing a testee to solve simple repetitive tasks, so-called speed tests. Existing approaches for adjusting
the time limit are either intrinsically nonadaptive or lack theoretical foundation. By contrast, we propose a mathematically sound framework in which latent competency skills are represented by belief distributions on compact intervals.
The algorithm iteratively computes a new difficulty setting, such that the amount of belief that can be updated after feedback has been received is maximized. We provide theoretical analyses and show empirically that our method performs equally well or better than state of the
art baselines in a near-realistic scenario.