Universal design does not suffice to provide educational scenarios where each learner is efficiently supported in her individual learning needs in an inclusive way. Moreover, there is a need for a dynamic support that provides recommendations to learners to overcome the impasses encountered at the course execution that are not covered within the design of the course. In this approach, the design is used to build the skeleton of standard-based models which are dynamically updated according to learners' interactions over time with machine learning techniques. In particular, the focus of this Ph.D is the Accesible and Adaptive Module (A2M) which consists of a multi-agent architecture based on open software solutions and artificial intelligence techniques that makes a pervasive use of standards to i) model learners and resources accessibility features, ii) follow the course design, iii) generate the presentation of the information and iv) communicate with other systems. With the models obtained, dynamic recommendations are provided.
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