Understanding and optimizing spacing of learning events is a central topic in basic research in learning and memory and has widespread and substantial implications for learning and instruction in real-world settings. Spacing memory retrievals across time improves memory relative to massed practice – the well-known spacing effect. Most spacing research has utilized fixed (predetermined) spacing schedules. Some findings indicate advantages of expanding spacing intervals over equal spacing (e.g., Landauer & Bjork, 1978); however, evidence is mixed (e.g., Karpicke & Roediger, 2007). One potential account of differing findings is that spacing per se is not the primary determinant; rather learning may depend on interactions of spacing with an underlying variable of learning strength that varies for learners and items. If so, learning may be better optimized by adaptive schedules that change spacing in relation to a learner’s ongoing performance. In two studies, we investigated an adaptive spacing algorithm, Adaptive Response-Time-based Sequencing (ARTS; Mettler, Massey & Kellman, 2011) that uses response time along with accuracy in interactive learning to generate spacing. In Experiment 1, we compared adaptive scheduling with fixed schedules having either expanding or equal spacing. In Experiment 2, we compared adaptive scheduling to two fixed “yoked” schedules that were copied from adaptive participants; these equated average spacing and trial characteristics across conditions. In both experiments, adaptive scheduling outperformed fixed conditions at immediate and delayed tests of retention. No evidence was found for differences between expanding and equal spacing. The advantage of adaptive spacing in yoked conditions was primarily due to adaptation to individual items and learners. Adaptive spacing based on ongoing assessments of learning strength for individual items and learners yields greater learning gains than fixed schedules, a finding that helps to understand the spacing effect theoretically and has direct applications for enhancing learning in many domains.