Two Approaches to Generate Intelligent Teaching-Learning Systems Using Artificial Intelligence Techniques

The use of present pedagogical methods with Information and Communication Technologies produce a new quality that favors the task of generating, transmitting and sharing knowledge. That is the case of the pedagogical effect that produces the use of the Concept Maps, which are considered a learning technique as a way to increase meaningful learning in the sciences. It is 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 this internal knowledge explicit in a visual form that can easily be examined and shared. In this paper the authors present two different approaches to elaborate Intelligent Teaching-Learning Systems, in each approach Concept Maps and Artificial Intelligence are combined, using in the first one the Case-Based Reasoning and in the other Bayesian Networks as a knowledge representation forms and inference mechanisms for the decision making, supporting the Student Model. The authors also show the facilities and the difficulties they had using each Artificial Intelligence technique combined with Concept Maps. The proposed models have been implemented in the computational systems HESEI and MacBay, whose have been successfully used in the Teaching-Learning process by laymen in the Computer Science field to generate them owns adaptive systems.