Predicting Learning Styles in a Conversational Intelligent Tutoring System

This paper presents Oscar, a conversational intelligent tutoring system (CITS) which dynamically predicts and adapts to a student's learning style throughout the tutoring conversation. Oscar aims to mimic a human tutor to improve the effectiveness of the learning experience by leading a natural language tutorial and modifying the tutoring style to suit an individual's learning style. Intelligent solution analysis and support have been incorporated to help students establish a deeper understanding of the topic and boost confidence. Oscar CITS with its natural dialogue interface and classroom tutorial style is more intuitive to learners than learning systems designed specifically to capture learning styles. An initial study is reported which produced encouraging results in predicting several learning styles and positive test score improvements in all students across the sample.