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.

Autonomous agents are an important area of research in the sense that they are proactive, and include: goal-directed and communication capabilities. Furthermore each goals of the agent are constantly changing in a dynamic environment. Part of the challenge is to automate the process corresponding to each agent in order that they find their own objectives. Agents do not have to work individually, but can work with others and develop a coordinated group of actions. These agents are highly appreciated, when real time problems are involved, meaning that an agent must be able to react within a specific time interval, considering external events. Our work focuses on the design of a multi-agent architecture consisting of autonomous agents capable of acting through a goal-directed with: a) constraints, b) real-time, and c) with incomplete knowledge of the environment. This paper shows a model of collaborative agents architecture that share a common knowledge source, allowing knowledge of the environment; where we analyze it and its changes, choosing the most promising way for achieving the goals of the agent, in order to keep the whole system working, even if a fault occurs.

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.

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Academic performance of recently accepted students is one of the main issues in Higher Level Institutions since first scholar periods trend to be the most difficult ones for students. Some institutions offer leveling courses to develop students basic knowledge for later courses. However, it is not clear if these help students in more advanced courses. This work presents an analysis, using decision trees, for predicting marks in two mathematics courses based on different criteria of the performance on a previous leveling course. This allows finding the factors that impact in the marks obtained in posterior courses and determining if the leveling one is helping students to improve their academic performance.