Artificial intelligence approach to support statistical quality control teaching Marcelo Menezes Reis a, * , Edson Pacheco Paladini b , Suresh Khator c , Willy Arno Sommer a a Computer Science and Statistics Department, Universidade Federal de Santa Catarina, Campus Universita ´ rio, Trindade, Floriano ´ polis, SC 88040-900, Brazil b Production Engineering Department, Universidade Federal de Santa Catarina, Campus Universita ´ rio, Trindade, Floriano ´ polis, SC 88040-900, Brazil c Industrial and Management Systems Engineering, University of South Florida, Tampa, FL 33620-5350, USA Received 17 July 2003; accepted 13 October 2004 Abstract Statistical quality control – SQC (consisting of Statistical Process Control, Process Capability Studies, Acceptance Sampling and Design of Experiments) is a very important tool to obtain, maintain and improve the Quality level of goods and services produced by an organization. Despite its importance, and the fact that it is taught in technical and college courses, as well as in companiesÕ training sectors, SQC has been largely misused. An inappropriate teaching approach may be the cause of such problem; therefore it has motivated the development of a model for SQC teaching, allowing its learners to correctly apply SQC tech- niques. After a survey regarding the concept needed to correctly apply SQC, its use and teaching/training methods, the modelÕs contents and methodology were defined. We also realized the opportunity of incor- porating a computer environment for the model, permitting the practice of the needed SQC concepts and skills. An Artificial Intelligence approach was used to develop the computer environment, resulting in an Intelligent Tutoring System, the STCEQ. The paper discusses the main characteristics of the system, its functioning, benefits of using such a system and the results we obtained while using this system. Ó 2004 Elsevier Ltd. All rights reserved. 0360-1315/$ - see front matter Ó 2004 Elsevier Ltd. All rights reserved. doi:10.1016/j.compedu.2004.10.016 * Corresponding author. Fax: +55 48 331 9770. E-mail address: marcelo@inf.ufsc.br (M.M. Reis). www.elsevier.com/locate/compedu Computers & Education 47 (2006) 448–464