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Adaptive behavior is one of the main challenges in building computerized systems, especially in the case of systems which are delivering information to the end users. Indeed, since the information overload has become the main drawback for the future development of such systems (cf. Big Data challenge), there is a huge movement in the research community to develop concepts for better adaptation of the form and size of information that will be delivered to a user (usually taking different forms of the personalization). However, the main effort has been dedicated to the contextualization of the user's task in order to determine what is the best way to tailor/adapt the presentation of information to the user, neglecting the role of the user's internal context, expressed as the user's (short-term) interest. The same is valid for the AR systems. In this tutorial we present novel results in modeling users' interest in the context of AR systems and demonstrate some practical results in realizing such an approach in a multisensor AR system based on the usage of the see-through AR glasses. Due to the need for continuously adapt the AR content to the user's interest, such models are facing many challenges in sensing the user's behavior (using acoustic-, video-, gesture- and bio-sensors), interpreting it as an interest and deciding in real-time what kind of the adaptation to perform. We argue that this lead to a new class of AR system that we coined as adaptive AR (AR) systems.This work has been partially realized within the scope of the FP7 ICT research project ARtSENSE (www.artsense.eu), that is developing new AR concepts for improving personalized museum's experience. The tutorial will present practical results from applying the approach in three cultural heritage institutions in Europe (Paris, Madrid and Liverpool).