AI techniques in the implementation of distance learning: A proposal

Abstract

Education via distance learning through the application of computers has reasonably evolved with the advent and merging of new technologies. In the early systems of computer-aided instruction (CAI) and computer-based training (CBT) the instruction was not focussed on the individual learner’s needs where the learner’s ability was not considered. However, learners advance from one module to another based on script-like materials. Although somewhat effective, we find these systems still lack individualized attention, as compared to learning form a human instructor where learners needs will be provided for. We have developed a framework consisting of five major components: student model, domain expert model, interest generator, tutoring model and user interface, for distance learning. We are proposing the use of artificial intelligence (AI) techniques in three components: student, tutoring and domain expert. By doing so, we hope that the current state of the learner is hypothesized in a knowledge base and instruction is individualized for the learner based on the knowledge learned about the learner. This paper will provide a brief overview of distance learning, a short discussion of the models used, and a description of applicable AI techniques that supports the implementation of effective and efficient distance learning systems.