It has long been recognized that in order to build a good system in which a person and a machine cooperate to perform a task it is important to take into account some significant characteristics of people. These characteristics are used to build some kind of a "user model". Traditionally, the model that is built is a model of a canonical (or typical) user. But often individual users vary so much that a model of a canonical user is insufficient. Instead, models of individual users are necessary. This article presents some examples of situations in which individual user models are important. It also presents some techniques that make the construction and use of such models possible. These techniques all reflect a desire to place most of the burden of constructing the models on the system, rather than on the user. This leads to the development of models that are collections of good guesses about the user. Thus some kind of probabilistic reasoning is necessary. And as the models are being used to guide the underlying system, they must also be monitored and updated as suggested by the interactions between the user and the system. The performance of one system that uses some of these techniques is discussed. 1.

Adaptive systems provide personalised services in accordance with their knowledge or assumptions about each interacting user. Such systems adapt their own behaviour to find the optimal response to a specific user need or goal. In adaptive e‐learning systems, personalisation is based upon the premise that distinct learners follow distinct strategies during learning and show distinct preferences in the consumption of learning materials. Indeed, adaptive e‐learning systems have proven to be more efficient than non‐adaptive approaches in some application domains and for some types of learners. An efficient adaptive system is capable of deciding autonomously what, how, when and why to personalise. Though, the last two aspects (when and why) are mostly triggered through its environment. Therefore, accurate internal models of the system’s real world are of major importance, i.e. the better the system knows the targets in its environment, the higher its capability of adapting successfully. First of all,