The Present and Future of Intelligent Tutoring Systems

  • Ernesto Costa
Conference paper
Part of the NATO ASI Series book series (volume 96)


The goals of this chapter are twofold. First, we will show how the use of machine learning techniques can greatly improve the dynamic construction and updating of student models. Then, we will discuss the underlying principles of the traditional design paradigm for Intelligent Tutoring Systems (ITSs) and will argue that they are, partially, responsible for the difficulties felt by present day ITSs. We maintain the thesis that the teaching (and learning) situation is a particular instance of the general case of interaction among intelligent agents. As a consequence of this point of view, we sustain that new principles for building ITSs are needed, based on the idea of belief systems.


ACM Artificial Intelligence belief systems diagnosing discrimination trees expert system explanation-based learning intelligent tutoring systems knowledge communicating system knowledge representation machine learning methodology student model student module subtraction tutorial strategies 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Allen, J., Natural Language Understanding, Benjamin/Cummings Publishing Co., Menlo Park, USA. 1987.Google Scholar
  2. 2.
    Carbonnell, J. and Langley, P. Machine Learning, in Encyclopaedia of Artificial Intelligence, S. Shapiro (Ed.), John Wiley and Sons, New York, USA. 1987.Google Scholar
  3. 3.
    Carbonnell, J.R. and Collins, A “Natural semantics in artificial intelligence.” Proceedings of the Third International Joint Conference on Artificial Intelligence, Stanford, California 1973.Google Scholar
  4. 4.
    Coelho, H. and Cotta, J. PROLOG by example, Springer-Verlag, New York, USA. 1988.MATHGoogle Scholar
  5. 5.
    Costa, E. Artificial intelligence and education: the role of knowledge in teaching Proceedings of the European Working Session on Learning - EWSL-86, Orsay, France. 1986.Google Scholar
  6. 6.
    Costa, E., Duchénoy, S. and Kodratoff, Y. A resolution based method for discovering students’ misconceptions in Artificial intelligence and human learning: intelligent computer- aided instruction, chapter 9, John Self (Ed), Chapman and Hall, London. 1988.Google Scholar
  7. 7.
    DeJong, G. and Mooney, R. Explanation-based learning: an alternative view Machine Learning, 1, n2, 145–176. 1986.Google Scholar
  8. 8.
    Gilmore, D. and Self, J. The application of machine learning to intelligent tutoring systems in Artificial intelligence and human learning: intelligent computer-aided instruction, chapter 11, John Self (Editor), Chapman and Hall, London. 1988.Google Scholar
  9. 9.
    Kedar-Cabelli, S. and McCarthy, L. Explanation-based generalization as resolution theorem proving Proceedings of the Fourth International Workshop on Machine Learning, University of California, Irvine, pp. 383–389. 1987.Google Scholar
  10. 10.
    Langley, P., Ohlsson S. and Sage S. A machine learning approach to student modelling Technical Report CMU-RI-TR-84-7, Carnegie-Mellon University, Robotics Institute. 1984.Google Scholar
  11. 11.
    Mandl, H. and Lesgold, A. Learning issues for intelligent tutoring systems, Springer-Verlag, New York, USA. 1988.Google Scholar
  12. 12.
    Michalsky, R. Understanding the nature of learning: issues and research directions in Machine Learning: an artificial intelligence approach, Michalsky, R. Carbonnell, J. and Mitchell, T. (Eds.), chapter 1, Morgan Kaufman, Los Altos, USA. 1986.Google Scholar
  13. 13.
    Mitchell, T., Keller, R. and Kedar-Cabelli, S. “Explanation-based learning: a unifying view” Machine Learning, 1, nl, 47–80. 1986.Google Scholar
  14. 14.
    O’Shea, T. and Self, J. Learning and teaching with computers: artificial intelligence in education, Harvester Press, UK. 1983.Google Scholar
  15. 15.
    Poison, M. and Richardson, J. (Eds), Foundations of Intelligent Tutoring Systems, Lawrence Erlbaum Associates Pub., Hillsdale, USA. 1988.Google Scholar
  16. 16.
    Stevens, A and Collins, A The goal structure of a socratic tutor. Proceedings of the national ACM Conference, Seattle, Washington. Association for Computing Machinery, New York. 1977.Google Scholar
  17. 17.
    Suchman, L., Plans and situated actions: the problems of human machine communication, Cambridge University Press, Cambridge, USA. 1987.Google Scholar
  18. 18.
    Viccari, R., An intelligent tutoring system for the PROLOG language, Ph. D. Thesis,. 1988.Google Scholar
  19. 19.
    Wenger, E., Artificial Intelligence and tutoring systems: computational and cognitive approaches to the communication of knowledge, Morgan Kaufman Pub. Inc., Los Altos, USA. 1987.Google Scholar
  20. 20.
    Wilks, Y. and Bien, J., Beliefs, points of view and multiple environments Cognitive Science, 7, 95–116. 1983.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1992

Authors and Affiliations

  • Ernesto Costa
    • 1
  1. 1.Dept. Engenhavia ElectrotechnicaUniversidade CoimbraPortugal

Personalised recommendations