Although investigators have shown that random scheduling of several tasks enhances learning more than blocked scheduling does, the advantages of random scheduling may be limited because it does not take into account the nominal difficulty of each task, the difference in difficulty between tasks, and the skill level of the learner in that type of schedule. The authors propose 2 new algorithms for adaptively determining the nominal difficulty and the number of trials for each task on the basis of both current and delayed performance of the learner (N = 48). The authors tested the adaptive algorithms in a 2 x 2 factorial design, and they show that the algorithms outperform random scheduling when performance is measured on a delayed retention test.