Skip to main content

The Practical Use of Artificial Intelligence in Automated Tutoring: Current Status and Impediments to Progress

  • Chapter
Understanding Literacy and Cognition
  • 86 Accesses

Abstract

The use of computer assisted instruction (CAI) has become increasingly common in educational settings ranging from preschool through college. Advances in artificial intelligence (AI), including improved capabilities for representation, organization, and application of knowledge, have simultaneously occurred. Intelligent CAI (ICAI), which addresses the problem of automating the teaching process, represents a relatively new and rapidly expanding area of artificial intelligence research. This paper focusses on the practicality of constructing knowledgeable and responsive intelligent computer assisted instruction by describing necessary components, useful techniques, and existing ICAI systems.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  • Abelson, H., & diSessa, A. (1981). Turtle geometry: The computer as a medium for exploring mathematics. Cambridge, MA: MIT Press.

    Google Scholar 

  • Allen, J. F., & Perrault, C. R. (1980). Analysing intention in utterances. Artificial Intelligence, 15, 143–178.

    Article  Google Scholar 

  • Anderson, J. R. (1983). The architecture of cognition. Cambridge, MA: Harvard University Press.

    Google Scholar 

  • Barrie, J. B. (1989). Using granularity hierarchies for strategy recognition. Unpublished Master of Science Thesis, Department of Computational Science, University of Saskatchewan, Saskatoon, Canada.

    Google Scholar 

  • Barwise, J., & Perry, J. (1983). Situations and attitudes. Cambridge, MA: MIT Press.

    Google Scholar 

  • Begg, I. M., & Hogg, I. (1987). Authoring systems for CAI. In G. P. Kearsley (Ed.), Artificial intelligence and instruction (pp. 323–346 ). Don Mills, Ontario: Addison-Wesley.

    Google Scholar 

  • Bobrow, D. G. (Ed.). (1985). Qualitative reasoning about physical systems. Cambridge, MA: MIT Press.

    Google Scholar 

  • Bobrow, D. G., & Winograd, T. (1977). An overview of KRL: A knowledge representation language. Cognitive Science, 1, 3–46.

    Article  Google Scholar 

  • Brachman, R. J., & Schmölze, J. G. (1985). An overview of the KL-ONE knowledge representation system. Cognitive Science, 9, 171–216.

    Article  Google Scholar 

  • Brown, J. S., & Burton, R. R. (1978). Diagnostic models for procedural bugs in basic mathematical skills. Cognitive Science, 2, 155–192.

    Article  Google Scholar 

  • Brown, J. S., Burton, R. R., & Bell, A. (1975). SOPHIE: A step toward a reactive learning environment. International Journal of Man-Machine Studies, 7, 675–696.

    Article  Google Scholar 

  • Burton, R. R. (1982). Diagnosing bugs in a simple procedural skill. In D. Sleeman & J. S. Brown (Eds.), Intelligent tutoring systems (pp. 157–184 ). New York: Academic Press.

    Google Scholar 

  • Burton, R. R., & Brown, J. S. (1982). An investigation of computer coaching for informal learning activities. In D. Sleeman & J. S. Brown (Eds.), Intelligent tutoring systems (pp. 79–98 ). New York: Academic Press.

    Google Scholar 

  • Carbonell, J. R. (1970). AI in CAI: An artificial intelligence approach to computer aided instruction. IEEE Transactions on Man-Machine Systems, 11, 190–202.

    Article  Google Scholar 

  • Cercone, N., & McCalla, G. I. (1986). Accessing knowledge through natural language. In M. C. Yovits (Ed.), Advances in Computers (Vol. 25 ) (pp. 1–99 ). New York: Academic Press.

    Google Scholar 

  • Clancey, W. J. (1982). Tutoring rules for guiding a case method dialog. In D. Sleeman & J. S. Brown (Eds.), Intelligent tutoring systems (pp. 201–226 ). New York: Academic Press.

    Google Scholar 

  • Cohen, P. R. (1978). Planning speech acts (Computer Science Tech. Rep. No. 118 ). Toronto: University of Toronto.

    Google Scholar 

  • Cohen, R. (1984, May). A theory of discourse coherence for argument understanding. Proceedings of the Fifth National Conference of the Canadian Society for Computational Studies of Intelligence (CSCSI). London, Ontario, Canada.

    Google Scholar 

  • Colbourn [Jones], M. (1984). Computer-based diagnosis of learning disabilities: A prototype. Unpublished Master of Education Thesis, Department for the Education of Exceptional Children, University of Saskatchewan, Saskatoon, Canada.

    Google Scholar 

  • Escott, J. A. (1988). Problem solving by analogy in novice programming. Unpublished Master of Science Thesis, Department of Computational Science, University of Saskatchewan, Saskatoon, Canada.

    Google Scholar 

  • Farrell, R. G., Anderson, J. R., & Reiser, B. J. (1984). An interactive computer-based tutor for LISP. Proceedings of the 5th Annual Conference of the American Association for Artificial Intelligence (AAAI) (pp. 106–109 ). Los Altos, CA: Morgan Kaufmann.

    Google Scholar 

  • Finzer, W., & Gould, L. (1984). Programming by rehearsal. Byte, 9, 187–214.

    Google Scholar 

  • Finzer, W., & Gould, L. (1984). Programming by rehearsal. Byte, 9, 187–214.

    Google Scholar 

  • Forgy, C. L. (1981). OPS5 User’s Manual. (Computer Science Tech. Rep. No. CS81–135) Pittsburgh: Carnegie Mellon University.

    Google Scholar 

  • Goforth, D., & McCalla, G. (1984). Lepus: A language to support student learning in non-mathematical domains. Association for Educational Data System Journal, 17, 14–29.

    Google Scholar 

  • Goldstein, I. P. (1982). The genetic graph: A representation for the evolution of procedural knowledge. In D. Sleeman & J. S. Brown (Eds.), Intelligent tutoring systems (pp. 51–78 ). New York: Academic Press.

    Google Scholar 

  • Greer, J. E. (1986, June). A new CAI authoring methodology. Proceedings of the National Educational Computing Conference, San Diego, CA.

    Google Scholar 

  • Grosz, B. (1977). The representation and use of focus in a system for understanding dialogues. Proceedings of the 5th International Joint Conference on Artificial Intelligence (IJCAI) (pp. 67–76 ). Los Altos, CA: Morgan Kaufmann.

    Google Scholar 

  • Halliday, M. A. K. (1964). The linguistic sciences and language teaching. London: Longmans.

    Google Scholar 

  • Harmon, P. ( 1986, October-November). The cost effectiveness of tools. Expert Systems Strategies.

    Google Scholar 

  • Hayes-Roth, B. (1985). A blackboard architecture for control. Artificial Intelligence, 26, 251–321.

    Article  Google Scholar 

  • Hendrix, G., Sacerdoti, E., Sagalowicz, D., & Slocum, J. (1978). Developing a natural language interface to complex data. ACM Transactions on Database Systems, 3, 105–147.

    Article  Google Scholar 

  • Horowitz, E. (1986, June). Instructional software development tools. Proceedings of the National Educational Computing Conference, San Diego, CA.

    Google Scholar 

  • Huang, X. (1987). Finding language errors and program equivalence in an automated programming advisor. Unpublished Master of Science Thesis, Department of Computational Science, University of Saskatchewan, Saskatoon, Canada.

    Google Scholar 

  • Inhelder, B., & Piaget, J. (1958). The growth of logical thinking from childhood to adolescence. New York: Basic Books.

    Book  Google Scholar 

  • Johnson, W. L., & Soloway, E. (1984). Intention-based diagnosis of programming errors. Proceedings of the 5th Annual Conference of the American Association for Artificial Intelligence (AAAI) (pp. 162–168 ). Los Altos, CA: Morgan Kaufmann.

    Google Scholar 

  • Kehler, T. P., & Clemenson, C. D. (1984). An application development system for expert systems. System Software, 3, 212–224.

    Google Scholar 

  • Littman, D., & Soloway, E. (1986, October). Toward an empirically-based process model for a machine programming tutor. Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, Atlanta, GA.

    Google Scholar 

  • Macmillan, S. A., & Sleeman, D. H. (1987). An architecture for a self- improving instructional planner for intelligent tutoring systems. Computational Intelligence, 3, 17–27.

    Article  Google Scholar 

  • McCalla, G. I. (1978). An approach to the organization of knowledge for the modelling of conversation. (Computer Science Tech. Rep. No. 78–4 ) Vancouver: University of British Columbia.

    Google Scholar 

  • McCalla, G. I., Bunt, R. B., & Harms, J. J. (1986). The design of the SCENT automated advisor. Computational Intelligence, 2, 76–91.

    Article  Google Scholar 

  • McCalla, G. I., Peachey, D. R., & Ward, B. (1982). An architecture for the design of large scale intelligent teaching systems. Proceedings of the Fourth National Conference of the Canadian Society for Computational Studies of Intelligence (CSCSI). Saskatoon, Canada.

    Google Scholar 

  • McDermott, D. (1982). A temporal logic for reasoning about plans and actions. Cognitive Science, 6, 101–155.

    Article  Google Scholar 

  • McDermott, D., & Doyle, J. (1980). Non-monotoniclogic. Artificial Intelligence, 13, 27–39.

    Article  Google Scholar 

  • Michalski, R. S., Carbonell, J. G., & Mitchell, T. M. (1984). Machine learning: An artificial intelligence approach. Palo Alto, CA: Tioga Publishing.

    Google Scholar 

  • Michalski, R. S., Carbonell, J. G., & Mitchell, T. M. (1986). Machine learning (Vol. 2 ). Palo Alto, CA: Tioga Publishing.

    Google Scholar 

  • Ng, T. H. (1987). Dynamic planning of blackboard focus shifts in an automated debugging system. (Computational Science Tech. Rep. No. 87–3 ) Saskatoon: University of Saskatchewan.

    Google Scholar 

  • O’ Shea, T. (1982). A self-improving quadratic tutor. In D. Sleeman & J. S. Brown (Eds.), Intelligent tutoring systems (pp. 309–336 ). New York: Academic Press.

    Google Scholar 

  • Papert, S. (1980). Mindstorms. New York: Basic Books.

    Google Scholar 

  • Peachey, D. R., & McCalla, G. I. (1986). Using planning techniques in intelligent tutoring systems. International Journal of Man-Machine Studies, 24, 77–98.

    Article  Google Scholar 

  • Pospisil, P. (1988). Diagnosing strategy errors in SCENT. Unpublished Master of Science Thesis, Department of Computational Science, University of Saskatchewan, Saskatoon, Canada.

    Google Scholar 

  • Reiter, R. (1980). A logic for default reasoning. Artificial Intelligence, 13, 81–132.

    Article  Google Scholar 

  • Reiter, R., & de Kleer, J. (1987). Foundations of assumption-based truth maintenance systems. Proceedings of the 6th National Conference on Artificial Intelligence (AAAI). Los Altos, CA: Morgan Kaufmann.

    Google Scholar 

  • Riesbeck, C., & Schank, R. (1976). Comprehension by computer: Expectation- based analysis of sentences in context. (Computer Science Tech. Rep. No. 78 ). New Haven, CT: Yale University.

    Google Scholar 

  • Skemp, R. R. (1979). Intelligence learning and action. New York: John Wiley.

    Google Scholar 

  • Small, S. (1980). Word expert parsing: A theory of distributed word-based natural language understanding. (Tech. Rep. No. 954 ) Baltimore, MD: University of Maryland.

    Google Scholar 

  • Spada, H., & Kempf, W. F. (Eds.). (1977). Structural models of thinking and learning: Proceedings of the 7th IPN-Symposium on Formalized Theories of Thinking and Learning and Their Implications for Science Instruction. Vienna: Hans Huber.

    Google Scholar 

  • Stefik, M. J., Bobrow, D. G., Mittal, S., & Conway, L. (1983). Knowledge programming in LOOPS: Report on an experimental course. Artificial Intelligence, 4, 3–14.

    Google Scholar 

  • Taylor, B., & Rosenberg, R. (1975). A case-driven parser for natural language. American Journal of Computational Linguistics. (Microfiche No. 31)

    Google Scholar 

  • Van Lehn, K. (1982). Bugs are not enough: Empirical studies of bugs, impasses and repairs in procedural skills. Journal of Mathematical Behaviour, 3, 3–72.

    Google Scholar 

  • Wasson, B. J. (1985). Student models: The genetic graph approach. (Computer Science Tech. Rep. No. CS-85-10) Waterloo: University of Waterloo.

    Google Scholar 

  • Winograd, T. (1972). Understanding natural language. Toronto: Academic Press.

    Google Scholar 

  • Woolf, B., & McDonald, D. D. (1984). Context dependent transitions in tutoring discourse. Proceedings of the 5th Annual Conference of the American Association for Artificial Intelligence (AAAI) (pp. 355–361 ). Los Altos, CA: Morgan Kaufmann.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 1989 Plenum Press, New York

About this chapter

Cite this chapter

McCalla, G.I., Greer, J.E. (1989). The Practical Use of Artificial Intelligence in Automated Tutoring: Current Status and Impediments to Progress. In: Leong, C.K., Randhawa, B.S. (eds) Understanding Literacy and Cognition. Springer, Boston, MA. https://doi.org/10.1007/978-1-4684-5748-3_7

Download citation

  • DOI: https://doi.org/10.1007/978-1-4684-5748-3_7

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4684-5750-6

  • Online ISBN: 978-1-4684-5748-3

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics