Abstract
The use of pedagogical methods combined with Information and Communication Technologies produces a new quality that favors the task of generating, transmitting and sharing knowledge. In that case we have the pedagogical effect that produces the use of Concept Maps, which are considered a learning technique as a way to increase meaningful learning in the sciences. Also used for the knowledge management as an aid to personalize the Teaching-Learning process, to exchange knowledge, and to learn how to learn. Concept Maps provides a framework for making the internal knowledge explicit in a visual form that can easily be examined and shared. In this paper the authors present different approaches to elaborate Intelligent Systems, applied in some areas, in each approach Concept Maps and Artificial Intelligence are combined, using in the first one the Case-Based Reasoning and in the other Bayesian Nets as a knowledge representation forms and inference mechanisms for the decision making, supporting the Student Model. The authors also show the use of other techniques like Petri Nets, Neural Nets, and Fuzzy Cognitive Maps with the goal of the Interface Adaptability. The proposed models have been implemented in computational systems that have been successfully used by laymen in the Computer Science field to generate them owns adaptive systems.