Adaptive tutoring in an intelligent conversational agent system

Authors:
Annabel Latham;Keeley Crockett;David McLean;Bruce Edmonds
Affiliations:
Intelligent Systems Group, School of Computing, Mathematics & Digital Technology, Manchester Metropolitan University, Manchester, UK;Intelligent Systems Group, School of Computing, Mathematics & Digital Technology, Manchester Metropolitan University, Manchester, UK;Intelligent Systems Group, School of Computing, Mathematics & Digital Technology, Manchester Metropolitan University, Manchester, UK;Centre for Policy Modelling, Manchester Metropolitan University, Manchester, UK
Venue:
Transactions on Computational Collective Intelligence VIII
Year:
2012

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Abstract

This paper describes an adaptive online conversational intelligent tutoring system (CITS) called Oscar that delivers a personalised natural language tutorial. During the tutoring conversation, Oscar CITS dynamically predicts and adapts to a student's learning style. Oscar CITS aims to mimic a human tutor by using knowledge of learning styles to adapt its tutoring style and improve the effectiveness of the learning experience. Learners can intuitively explore and discuss topics in natural language, helping to establish a deeper understanding of the topic and boost confidence. An initial study into the adaptation to learning styles is reported which produced encouraging results and positive test score improvements. The results show that students experiencing a tutorial adapted to suit their learning styles performed significantly better than those experiencing an unsuited tutorial.