A framework integrating case-based reasoning (CBR) and meta-learning is proposed in this paper as the underlying methodology enabling self-improving intelligent tutoring systems (ITSs). Pedagogical strategies are stored in cases, each dictating, given a specific situation, which tutoring action to make next. Reinforcement learning is used to improve various aspects of the CBR module Ð cases are learned and retrieval and adaptation are improved, thus modifying the pedagogical strategies based on empirical feedback on each tutoring session. To minimize canceling out effects due to the multiple strategies used for meta-learning Ð for example, the learning result of one strategy undoes or reduces the impact of the learning result of another strategy, a principled design that is both cautious and prioritized is put in place. An ITS application, called Intelligent Learning Material Delivery Agent (ILMDA), has been implemented, powered by this framework, on introductory computer science topics, and deployed at the Computer Science and Engineering Department of the University of Nebraska. Studies show the feasibility of such a framework and impact analyses are reported on pedagogical strategies and outcomes.