Governments and institutions are facing the new demands of a rapidly changing society. Among many significant trends, some facts should be considered (Silverstein, 2006): (1) the increment of number and type of students; and (2) the limitations imposed by educational costs and course schedules. About the former, the need of a continuous update of knowledge and competences in an evolving work environment requires life-long learning solutions. An increasing number of young adults are returning to classrooms in order to finish their graduate degrees or attend postgraduate programs to achieve an specialization on a certain domain. About the later, due to the emergence of new types of students, budget constraints and schedule conflicts appear. Workers and immigrants, for instance, are relevant groups for which educational costs and job incompatible schedules could be the key factor to register into a course or to give up a program after investing time and effort on it. In order to solve the needs derived from this social context, new educational approaches should be proposed: (1) to improve and extend the online learning courses, which would reduce student costs and allows to cover the educational needs of a higher number of students, and (2) to automate learning processes, then reducing teacher costs and providing a more personalized educational experience anytime, anywhere. As a result of this context, in the last decade an increasing interest on applying computer technologies in the field of Education has been observed. On this regard, the paradigms of the Artificial Intelligence (AI) field are attracting an special attention to solve the issues derived from the introduction of computers as supporting resources of different learning strategies. In this paper we review the state-of-art of the application of Artificial Intelligence techniques in the field of Education, focusing on (1) the most popular educational tools based on AI, and (2) the most relevant AI techniques applied on the development of intelligent educational systems.
The field of Artificial Intelligence can contribute with interesting solutions to the needs of the educational domain (Kennedy, 2002). In what follows, the type of systems that can be built based on AI techniques are outlined.
Key Terms in this Chapter
Computer Supported Collaborative Learning (CSCL): A research topic on supporting collaborative learning methodologies with the help of computers and collaborative tools.
Student Models: Representation of student behavior and degree of competence in terms of existing background knowledge about a domain.
Automatic Evaluation Systems: Applications focused on evaluating the strengths and weaknesses of students in different learning activities through assessment tests.
Ontologies: A set of concepts within a domain that capture and represent consensual knowledge in a generic way, and that they may be reused and shared across software applications.
Game-Based Learning: A new type of learning that combines educational content and computer games in order to improve the satisfaction and performance of students when acquiring new knowledge and skills.
Intelligent Tutoring Systems: A computer program that provides personalized/adaptive instruction to students without the intervention of human beings.
Software Agents: Software entities, such as software programs or robots, characterized by their autonomy, cooperation and learning capabilities.