Adaptive Interface Methodology for Intelligent Tutoring Systems

In a Teaching-Learning Process (TLP) teachers have to support student’s learning using diverse pedagogical resources. One of teachers’ task is to create personalized Learning Environments. Intelligent Tutoring Systems (ITS) try to imitate adaptation capacity of a human teacher. The Interface is the Learning Environment and the system stores knowledge that defines how to adapt it to respond to certain student’s characteristics. Adaptation is particularly important for TLP oriented to carriers of chronic diseases like Diabetes, which represent very heterogeneous groups of persons. This article presents a Methodology to model a TLP and to build an automatic adaptation (adaptive) mechanism for ITS Interfaces, based in a Neural Network [1]. The diabetes education was used as a case study to apply and validate the proposed methodology. The most important results of this work are presented here.