The Foundations and Architecture of Autotutor

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1452)


The Tutoring Research Group at the University of Memphis is developing an intelligent tutoring system which takes advantages of recent technological advances in the areas of semantic processing of natural language, world knowledge representation, multimedia interfaces, and fuzzy descriptions. The tutoring interaction is based on in-depth studies of human tutors, both skilled and unskilled. Latent semantic analysis will be used to semantically process and provide a representation for the student’s contributions. Fuzzy production rules select appropriate topics and tutor dialogue moves from a rich curriculum script. The production rules will implement a variety of different tutoring styles, from a basic untrained tutor to one which uses sophisticated pedagogical strategies. The tutor will be evaluated on the naturalness of its interaction, with Turing-style tests, by comparing different tutoring styles, and by judging learning outcomes.


Production Rule Latent Semantic Analysis Intelligent Tutoring System World Knowledge Human Tutor 
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.
    J. R. Anderson, A. T. Corbett, K. R. Koedinger, and R. Pelletier. Cognitive tutors: Lessons learned. The Journal of the Learning Sciences, 4:167–207, 1995. 335, 337CrossRefGoogle Scholar
  2. 2.
    B. S. Bloom. The 2 sigma problem: The search for methods of group instruction as effective as one-to-one tutoring. Educational Researcher, 13:4–16, 1984. 337Google Scholar
  3. 3.
    J. D. Bransford, S. R. Goldman, and N. J. Vye. Making a difference in people’s ability to think: Reflections on a decade of work and some hopes for the future. In R. J. Sternberg and L. Okagaki, editors, Influences on children, pages 147–180. Erlbaum, Hillsdale, NJ, 1991. 337Google Scholar
  4. 4.
    M. T. H. Chi, N. de Leeuw, M. Chiu, and C. LaVancher. Eliciting self-explanations improves understanding. Cognitive Science, 18:439–477, 1994. 338CrossRefGoogle Scholar
  5. 5.
    H. H. Clark and E. F. Schaefer. Contributing to discourse. Cognitive Science, 13:259–294, 1989. 337CrossRefGoogle Scholar
  6. 6.
    A. Collins. Teaching reasoning skills. In S. Chipman, J. Segal, and R. Glaser, editors, Thinking and learning skills, pages 579–586. Erlbaum, Hillsdale, NJ, 1985. 337Google Scholar
  7. 7.
    DARPA. Proceedings of the Sixth Message Understanding Conference (MUC-6). Morgan Kaufman Publishers, San Francisco, 1995. 335Google Scholar
  8. 8.
    P. Foltz. Latent semantic analysis for text-based research. Behavior Research Methods, Instruments, and Computers, 28:197–202, 1996. 336, 336Google Scholar
  9. 9.
    M. A. Gernsbacher, editor. Handbook of Psycholinguistics. Academic Press, San Diego, CA, 1994. 336Google Scholar
  10. 10.
    A. Graesser and L. Clark. Structures and procedures of implicit knowledge. Ablex, Norwood, NJ, 1985. 336Google Scholar
  11. 11.
    A. C. Graesser, N. K. Person, and J. P. Magliano. Collaborative dialogue patterns in naturalistic one-to-one tutoring. Applied Cognitive Psychology, 9:359–387, 1995. 337, 337, 338, 340CrossRefGoogle Scholar
  12. 12.
    J. Hirschberg and G. Ward. The interpretation of the high-rise question contour in english. Journal of Pragmatics, 24:407–412, 1995. 336CrossRefGoogle Scholar
  13. 13.
    V. Holland, J. Kaplan, and M. Sams. Intelligent language tutors. Erlbaum, Mahwah, NJ, 1995. 335Google Scholar
  14. 14.
    G. D. Hume, J. Michael, A. Rovick, and M. W. Evens. Hinting as a tactic in one-on-one tutoring. The Journal of the Learning Sciences, 5:23–47, 1996. 337CrossRefGoogle Scholar
  15. 15.
    W. Kintsch. Comprehension: A paradigm for cognition. Cambridge University Press, Cambridge, MA, in press. 336, 336Google Scholar
  16. 16.
    T. Landauer and S. Dumais. A solution to Plato’s problem: The latent semantic analysis theory of acquisition, induction, and representation of knowledge. Psychological Review, 104:211–240, 1997. 336, 336CrossRefGoogle Scholar
  17. 17.
    A. Lesgold, S. Lajoie, M. Bunzo, and G. Eggan. Sherlock: A coached practice environment for an electronics troubleshooting job. In J. H. Larkin and R. W. Chabay, editors, Computer-assisted instruction and intelligent tutoring systems, pages 201–238. Erlbaum, Hillsdale, NJ, 1992. 335Google Scholar
  18. 18.
    D. Massaro and M. Cohen. Perceiving talking faces. Psychological Science, 4:104–109, 1995. 335Google Scholar
  19. 19.
    D. Newman. Is a student model necessary? Apprenticeship as a model for ITS. In D. Bierman, J. Breuker, and J. Sandberg, editors, Artificial intelligence and education. IOS, Amsterdam, 1989. 338Google Scholar
  20. 20.
    A. S. Palinscar and A. Brown. Reciprocal teaching of comprehension-fostering and comprehension-monitoring activities. Cognition andInstruction,1:117–175,1984. 337CrossRefGoogle Scholar
  21. 21.
    C. Pelachaud, N. Badler, and M. Steedman. Generating facial expressions for speech. Cognitive Science, 20:1–46, 1996. 335, 336CrossRefGoogle Scholar
  22. 22.
    N. K. Person, A. C. Graesser, J. P. Magliano, and R. J. Kreuz. Inferring what the student knows in one-to-one tutoring: The role of student questions and answers. Learning and Individual Differences, 6:205–229, 1994. 337, 338CrossRefGoogle Scholar
  23. 23.
    N. K. Person, R. J. Kreuz, R. Zwaan, and A. C. Graesser. Pragmatics and pedagogy: Conversational rules and politeness strategies may inhibit effective tutoring. Cognition and Instruction, 13:161–188, 1995. 340CrossRefGoogle Scholar
  24. 24.
    B. Rogoff. Apprenticeship in thinking. Oxford University Press, NewYork, 1990. 337Google Scholar
  25. 25.
    B. Tversky and K. Hemenway. Categories of environmental scenes. Cognitive Psychology, 15:121–149, 1983. 336CrossRefGoogle Scholar
  26. 26.
    K. Van Lehn. Mind bugs: The origins of procedural misconceptions. MIT Press, Cambridge, MA, 1990. 335Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  1. 1.Department of PsychologyThe University of MemphisMemphis
  2. 2.Department of Mathematical SciencesThe University of MemphisMemphis
  3. 3.College of EducationThe University of MemphisMemphis

Personalised recommendations