Oscar: An intelligent conversational agent tutor to estimate learning styles

Intelligent tutoring systems are computer learning systems which personalise their learning content for an individual, based on learner characteristics such as existing knowledge. A recent extension to ITS is to capture student learning styles using a questionnaire and adapt subject content accordingly, however students do not always take the time to complete questionnaires carefully. This paper describes Oscar, a conversational intelligent tutoring system (CITS) which utilises a conversational agent to conduct the tutoring. The CITS aims to mimic a human tutor by dynamically estimating and adapting to a student´s learning style during a tutoring conversation. Oscar also offers intelligent solution analysis and problem support for learners. By implicitly modelling the student´s learning style during tutoring, Oscar can personalise tutoring to each individual learner to improve the effectiveness of the tutoring. The paper presents the novel methodology and architecture for constructing a CITS. An initial pilot study has been conducted in the domain of tutoring of undergraduate Science and Engineering students using the Index of Learning Styles ILS) model. The experiments to investigate the estimation of learning style have produced encouraging results in the estimation of learning style through a tutoring conversation.