An Agent-Based Intelligent Tutoring System: A Case Study in Legal Domain

Computer based learning gets more and more important in higher education. Particularly, in Legal domain, students have little chance to deal with realistic situations. One way to alleviate this problem is to provide Law students with real cases, rules, and viewpoints of which a given body of knowledge is often recognized as important to their successful learning. In this paper is proposed a novel approach to Intelligent Tutoring System (ITS) applied to Legal domain to address each of the above concerns. Then, it was defined an agentbased architecture to support multiple views of domain knowledge, improving the quality of student-ITS interactions and the learning success of the students. Each tutoring agent from the system contains a knowledge-based system that combines Case-Based Reasoning (CBR) and Rule-Based System (RBS). In addition, each agent adopts the Reinforcement Learning Algorithm aiming at identifying the best pedagogical strategy by considering the student profile. This paper focuses on both architecture and the mentioned Artificial Intelligence techniques into a Legal System.