Recent rise of Massive Open Online Courses (MOOCs) with unlimited participants, makes employing learning tools such as interactive simulations all but inevitable. Interactive simulations give students the opportunity to experiment with concrete examples and develop better understanding of concepts they have learned. However, some students do not learn well from this relatively unstructured form of interaction, suggesting the provision of adaptive support as a way to address this issue. This paper presents a formal evaluation of this approach. We describe the process of designing an intervention delivery mechanism for adding adaptive support to an exploratory interactive simulation. The experimental evaluation of the adaptive version of the simulation indicates that the adaptive support provided to students significantly improved their learning performance. Quantitative and qualitative evaluations of users' acceptance of the system are generally positive but pinpoint areas for improvement.

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The field of intelligent tutoring systems has successfully delivered techniques and applications to provide personalized coaching and feedback for problem solving in a variety of domains. The core of this personalized instruction is a student model; the ITS component in charge of assessing student traits and states relevant to tailor the tutorial interaction to specific student needs during problem solving. There are however, other educational activities that can help learners acquire the target skills and abilities at different stages of learning including, among others, exploring interactive simulations and playing educational games. This article describes research on creating student models that support personalization for these novel types of interactions, their unique challenges, and how AI and machine learning can help.

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