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Intelligent tutoring systems have been in existence for decades, and their characteristics can be beneficially applied in environments utilizing information and communication technology (ICT). The "intelligence" in these systems is seen through the way these systems adapt themselves to the characteristics of the students, such as speed of learning, specific areas in which the student excels as well as falls behind, and rate of learning as more knowledge is learned. In such intelligent learning environments, the agent or set of agents can be modeled to perform pedagogical tasks. This paper considers the necessary characteristics that constitute a good intelligent tutoring system. This paper introduces a framework incorporating an incremental machine-learning approach to capture 1) the dynamics of knowledge creation in the domain of interest and 2) the learned-knowledge content of the student over time. Some of the components of the proposed system are illustrated using examples from an introductory course on database design.