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This article describes the analysis, design and development of an Intelligent Learning System (ILS). The design of the ILS is based on a multi-agent architecture. This architecture includes reactive agents which represent the expertise of each of the necessary sub-skills in learning the application domain, which in the study case is structured programming. The ILS utilizes artificial intelligence techniques to implement the teaching-learning process using an inference engine based on a general didactic model. As a result, this system is termed as Intelligent Learning System with Learning Objects (ProgEst). ProgEst is carried out with the objective of providing the user with self-regulated learning strategies in addition to the knowledge of a determined domain. The case study includes situations related to: learning styles, knowledge domain (errors made) and affective-motivational state. The assessments shall determine: 1) what is to be explained, 2) level of detail and timing, 3) how and when to interrupt the student, and 4) the information to provide during the interaction.

This article discusses the aspects that are recommendable when designing an interface that includes a collaborative pedagogical agent within a context in which the collaborative learning process is reinforced by the task distribution process that goes with it. The concept of the intelligent tutoring system, conceived as a pedagogical interface agent (interface with human features that permits interaction between system and user), forms the basis of this study. The pedagogical agent is constituted by an intelligent tutoring system that makes a diagnosis adapted to needs of students, so as to improve the learning process. This is achieved by dynamic interaction on a system that has a collaborative and distributed interaction facility, in which the agent is conceived as an educational tool.

Why we have emotions and how they influence human behavior are two questions we constantly ask ourselves.According to researchers in the field, there are two kinds of information that the person receives to make a decision.This information is integrated to generate a whole. One comes from ourselves it is proprioceptive and refers toemotions; the other one is external, it comes from the environment. Thus, emotions are immersed in different areas ofa person’s life, including social, functional, and cognitive aspects. It has an important role in the decision-makingprocess. It is on this last point that this paper focuses its approach, which consists of including a cognitive structure ofemotions in a reactive behavior in order to enrich behavior, including the information generated through internalevaluation of the external physical stimuli. Our work is based on the OCC theory (named after the name of its authors:Ortony, Collins and Clore). This theory proposes that a decision-making process is affected through differentperspectives: a) goals and events, b) agents and their actions, and c) objects and their capacity for attraction. Thecase study is situated within a scenario that represents an approach that seeks to include a module of emotions withinthe design of the architecture of a videogame.

This research is framed within the affective computing, which explains the importance of emotions in human cognition (decision making, perception, interaction and human intelligence). Applying this approach to a pedagogical agent is an essential part to enhance the effectiveness of the teaching-learning process of an intelligent learning system. This work focuses on the design of the inference engine that will give life to the interface, where the latter is represented by a pedagogical agent. The inference engine is based on an affective-motivational model. This model is implemented by using artificial intelligence technique called fuzzy cognitive maps.