Adaptive learning techniques have typically scheduled practice using learners' accuracy and item presentation history. We describe an adaptive learning system (Adaptive Response Time Based Sequencing—ARTS) that uses both accuracy and response time (RT) as direct inputs into sequencing. Response times are used to assess learning strength and to determine mastery, making both fluency and accuracy goals of learning. ARTS optimizes spacing by expanding item recurrence intervals as an inverse function of RT. In Experiment 1, we compared ARTS to Atkinson’s (1972) Markov model system using geography learning and found substantially greater learning efficiency for ARTS. In Experiment 2, we deployed the system in a real learning setting. Third graders attending an online school mastered basic multiplication facts in about two hours using ARTS, outperforming a control group using standard instruction. These results suggest that response time-based adaptive learning has remarkable potential to enhance learning in many domains.