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An adaptation algorithm for an intelligent natural language tutoring system

<22>Presents a new adaptation algorithm within a conversational intelligent tutoring system, and its application to Felder-Silverman learning styles model. Algorithm uniquely combines strength of an individual's learning style preference with adaptive tutoring material for each tutorial question to provide 24/7 personalised learning using a conversational agent. Extension of core work from Ph.D. thesis of Latham (successfully defended November 2011). Comprehensive evaluation study in live HE environment demonstrates system robustness, scalability and positive difference in student learning experiences through flexible learning. MMU funding awarded to assess feasibility of commercialisation.