Elaborating the Context of Interactions in a Tutorial Dialog

  • Josephine Pelle
  • Roger Nkambou
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4031)


In Socratic tutorial dialogues the tutor selects the most appropriate question to be asked, based on assumptions about what the learner knows. However, there is no guarantee that the learner will understand the question. Indeed, the assumptions of the tutor are sometimes likely to be inaccurate. In this case, an appropriate action for the tutor is to revise its current GOAL as well as its current dialogue plan. In this paper, we present an instance of this issue in Prolog-tutor, a tutoring system for Logic Programming. Our contribution is an explicit address of the dialog management mechanism which supports the revision of the tutor’s intention in a Socratic dialogue. This is done using a combination of the theory of accommodation of communicative acts with the notion of revising intentions.


Bayesian Network Logic Program Learner Model Intelligent Tutoring System Knowledge Element 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Cawsey, A.: Planning Interactive Explanations. International Journal of Man-machine Studies 38(2), 169–200 (1993)CrossRefGoogle Scholar
  2. 2.
    Cawsey, A., Galliers, J., Logan, G., Reese, S., Sparck-Jones, K.: Revising Beliefs and Intentions: A Unified Framework for Agent Interaction. In: Prospects in artificial intelligence: AISB. IOS Press, Amsterdam (1993)Google Scholar
  3. 3.
    Chi, M.T., De Leeuv, N., Chiu, M.H., Lavancher, C.: Eliciting Self explanations improves understanding. Cognitive Sciences 18(3), 439–477 (1994)Google Scholar
  4. 4.
    Clancey, W.J.: Knowledge-Based Tutoring: The Guidon Program. MIT Press, Cambridge (1987)Google Scholar
  5. 5.
    Collins, A., Stevens, A.L.: Goals and strategies of inquiry teachers. In: Glaser, R. (ed.) Advances in instructional psychology, pp. 65–119. Lawrence Erlbaum Assoc., Hillsdale, NJ (1982)Google Scholar
  6. 6.
    Conati, C., Gertner, A., Van Lehn, K.: Using Bayesian Networks to Manage Uncertainty in Student Modeling. Journal of User Modeling and User-Adapted Interaction 12(4), 371–417 (2002)CrossRefMATHGoogle Scholar
  7. 7.
    Corbett, A.T., Anderson, J.R.: Knowledge Tracing: Modeling the Acquisition of Procedural Knowledge. User Modeling and User Adapted Interaction 4, 253–278 (1995)CrossRefGoogle Scholar
  8. 8.
    Cozman, F.G.: The JavaBayes system. The ISBA Bulletin 7(4), 16–21 (2001)Google Scholar
  9. 9.
    Dimitrova, V., Self, J., Brna, P.: Involving the Learner in Diagnosis - Potentials and Problems. In: Tutorial at Web Information Technologies: Research, Education and Commerce, MontPellier, France (2000)Google Scholar
  10. 10.
    Ginzburg, J.: Interrogatives: Questions, facts and dialogue. In: Handbook of Contemporary Semantic Theory. Blackwell, Oxford (1996)Google Scholar
  11. 11.
    Ginzburg, J.: Clarifying utterances. In: Proceedings of the Twente Workshop on the Formal Semantics and Pragmatics on Dialogues (1998)Google Scholar
  12. 12.
    Heffernan, N., Koedinger, K.: An Intelligent Tutoring System Incorporating a Model of an Experienced Human Tutor International Conference on Intelligent Tutoring System 2002, Biarritz, France. Springer, Heidelberg (2002)Google Scholar
  13. 13.
    Koedinger, K., Anderson, J., Hadley, W., Mark, M.: Intelligent Tutoring Goes To School in the Big City. Int. Journal of Artificial Intelligence in Education 8, 30–43 (1997)Google Scholar
  14. 14.
    Larsson, S., Cooper, R., Engdahl, E.: Question accommodation and information states ind Dialogue. In: Third Wokshop in Human-Computer Coversation, Bellagio (2000)Google Scholar
  15. 15.
    Moore, J., Paris, C.: Planning Text for Advisory Dialogues. Computational Linguistics 19(4), 651–694 (1993)Google Scholar
  16. 16.
    Pearson, N., Graesser, A., Kreuz, R., Pomeroy, V., Group, T.R.: Simulating human tutor dialog moves in AutoTutor. International Journal of Artificial Intelligence in Education 12, 23–39 (2001)Google Scholar
  17. 17.
    Razzaq, L., Heffernan, H.: Tutorial dialog in an equation solving intelligent tutoring system. In: Lester, J.C., Vicari, R.M., Paraguaçu, F. (eds.) ITS 2004. LNCS, vol. 3220, pp. 851–853. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  18. 18.
    Tchetagni, J., Nkambou, R.: A framework for the hierarchical representation of the learner model using Bayesian Networks, Biarritz, France. Springer, Heidelberg (2002)Google Scholar
  19. 19.
    Tchetagni, J., Nkambou, R., Kabanza, F.: Epistemological Remediation in Intelligent Tutoring Systems. In: Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, Ottawa, Canada (2004)Google Scholar
  20. 20.
    Tsovaltzi, D., Matheson, C.: Formalising hinting in tutorial dialogues. In: EDILOG: 6th workshop on the semantics and pragmatics of dialogue, Edinburgh (2002)Google Scholar
  21. 21.
    Woolf, B., Murray, T.: A Framework for Representing Tutorial Discourse. In: 10th joint conference on artificial intelligence (1987)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Josephine Pelle
    • 1
  • Roger Nkambou
    • 1
  1. 1.Université du Québec à MontréalMontréal, QuébecCanada

Personalised recommendations