Much research in Intelligent Tutoring Systems has explored how to provide on-demand hints, how they should be used, and what effect they have on student learning and performance. Most of this work relies on hints created by experts and assumes that all help provided by the tutor is correct and of high quality. However, hints may not all be of equal value, especially in open-ended problem solving domains, where context is important. This work argues that hint quality, especially when using data-driven hint generation techniques, is inherently uncertain. We investigate the impact of hint quality on students’ help-seeking behavior in an open-ended programming environment with on-demand hints. Our results suggest that the quality of the first few hints on an assignment is positively associated with future hint use on the same assignment. Initial hint quality also correlates with possible help abuse. These results have important implications for hint design and generation.