Response times on items can be used to improve item selection in adaptive testing provided that a probabilistic model for their distribution is available. In this research, the author used a hierarchical modeling framework with separate first-level models for the responses and response times and a second-level model for the distribution of the ability and speed parameters in the population of test takers. The framework allows the author to retrofit an empirical prior distribution for the ability parameter on each occurrence of a new response time. In an example with an adaptive version of the Law School Admission Test (LSAT), the author shows how this additional update of the posterior distribution of the ability leads to a substantial improvement of the ability estimator. Two ways of applying the procedure in real-world adaptive testing are discussed.