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In this paper, we describe an intelligent agent that delivers learning materials adaptively to different students, factoring in the usage history of the learning materials, the student static background profile, and the student dynamic activity profile. Our assumption is that through the interaction of a student going through a learning material (i.e., a topical tutorial, a set of examples, and a set of problems), our agent will be able to capture and utilize the student’s activity as the primer to select the appropriate example or problem to administer to the student. Even if the agent fails to do so, it is able to recover to provide a more appropriate example of problem based on what it learns from its failure. In addition, our agent monitors the usage history of the learning materials and derives empirical observations that improve its performance. We have built an end-to-end ILMDA infrastructure, with a GUI front-end, an agent powered by case-based reasoning (CBR), and a mySQL multi-database backend. We have also built a comprehensive simulator for our experiments. Preliminary experiments show the feasibility, correctness, and learning capability of ILMDA.