Artificial Intelligence Review

, Volume 4, Issue 4, pp 251–277 | Cite as

Intelligent tutoring systems: an overview

  • Hyacinth S. Nwana

Abstract

This is a non-expert overview of Intelligent Tutoring Systems (ITSs), a way in which Artificial Intelligence (AI) techniques are being applied to education. It introduces ITSs and the motivation for them. It looks at its history: its evolution from Computer-Assisted Instruction (CAI). After looking at the structure of a ‘typical’ ITS, the paper further examines and discusses some other architectures. Several classic ITSs are reviewed, mainly due to their historical significance or because they best demonstrate some of the principles of intelligent tutoring. A reasonably representative list of ITSs is also provided in order to provide a better appreciation of this vibrant field as well as reveal the scope of existing tutors. The paper concludes, perhaps more appropriately, with some of the author's viewpoints on a couple of controversial issues in the intelligent tutoring domain.

References

  1. Anderson, J. R. (1987) Production systems, learning and tutoring. In Production System Models of Learning and Development (eds D. Klahr, P. Langley & R. Neches). MIT Press, London, pp. 437–458.Google Scholar
  2. Anderson, J. R., Boyle, D. G. & Reiser, B. J. (1985a) Intelligent tutoring systems. Science 228, 456–462.Google Scholar
  3. Anderson, J. R. Boyle, D. F. & Yost, G. (1985b) The geometry tutor. In Proceedings of the 9th International Joint Conference on Artificial Intelligence, Los Angeles, CA, pp. 1–7.Google Scholar
  4. Anderson, J. R. & Reiser, B. J. (1985) The lisp tutor. Byte, 10(4).Google Scholar
  5. Attisha, M. G. & Yazdani, M. (1983) A micro-computer based tutor for teaching arithmetic skills. Instructional Science, 12, 333–342.Google Scholar
  6. Attisha, M. G. & Yazdani, M. (1984) An expert system for diagnosing children's multiplication errors. Instructional Science, 13, 79–92.Google Scholar
  7. Barchan, J., Woodmansee, B. J. & Yazdani, M. (1986) A prlog-based tool for French grammar analysis. Instructional Science, 14.Google Scholar
  8. Barr, A., Beard, M. & Atkinson, R. C. (1976) The computer as a tutorial laboratory: the Stanford BIP Project. International Journal of Man-Machine Studies, 8, 567–596.Google Scholar
  9. Barr, A. & Feigenbaum, E. A. (1982) The Handbook of Artificial Intelligence, Vol. 2. Kaufmann, Los Altos.Google Scholar
  10. Barzilay, A. (1985) SPIRIT: a flexible tutoring style in an intelligent tutoring system. In Artificial Intelligence Applications: The Engineering of Knowledge-Based Systems (ed. R. C. Weisbin). IEE Computer Society, North Holland.Google Scholar
  11. Blaine, L. H. (1982) EXCHECK. Handbook of Artificial Intelligence, Vol. 2 (eds A. Barr & E. A. Feigenbaum). Addison-Wesley, Reading, MA.Google Scholar
  12. Bloom, B. S. (1984) The 2 Sigma Problem: the search for methods of group instruction as effective as one-to-one tutoring. Educational Researcher 13, 4–16.Google Scholar
  13. Bobrow, D. G., Mittal, S. & Steffik, M. (1986) Expert systems: perils and promise. Communications of the ACM, 29, 880–893.Google Scholar
  14. Bonar, J. (1985) Understanding the Bugs of Novice Programmers. PhD Thesis, Dept of Computer and Information Science, University of Pittsburgh, Pittsburgh, PA.Google Scholar
  15. Bonnet, A. (1985) Artificial Intelligence: Promise and Performance. Prentice Hall, London.Google Scholar
  16. Breuker, J. (1987) Coaching in help systems. In Artificial Intelligence and Human Learning: Intelligent Computer-aided Instruction (ed. J. A. Seft) Chapman & Hall, London.Google Scholar
  17. Brown, J. S. (1985) Process versus product: a perspective on tools for communal and informal electronic learning. Journal of Educational Computing Research 1, 179–201.Google Scholar
  18. Brown, J. S. & Burton, R. E. (1978a) Diagnostic models for procedural bugs in basic mathematical skills. Cognitive Science, 2, 155–192.Google Scholar
  19. Brown, J. S. & Burton, R. R. (1978b) A paradigmatic example of an artificially intelligent instructional system. International Journal of Man-Machine Studies, 10, 323–339.Google Scholar
  20. Brown, J. S., Burton, R. R. & Bell, A. G. (1975) SOPHIE: a step towards a reactive learning environment. International Journal of Man-Machine Studies, 7, 675–696.Google Scholar
  21. Brown, J. S., Burton, R. R. & de Kleer, J. (1982) Pedagogical, natural language, and knowledge engineering techniques in SOPHIE I, II and III. In Intelligent Tutoring Systems (eds D. H. Sleeman & J. S. Brown). Academic Press, London, pp. 227–282.Google Scholar
  22. Brown, J. S., Burton, R. R. & Zydel, F. (1973) A model-driven question-answering system for mixed-initiative CAI. IEEE Transactions on Systems, Man, and Cybernetics, 3, 248–257.Google Scholar
  23. Burns, H. L. & Capps, C. G. (1988) Foundations of intelligent tutoring systems: an introduction. In Foundations of Intelligent Tutoring Systems (eds M. C. Polson & J. J. Richardson). Lawrence Erlbaum, London, pp. 1–19.Google Scholar
  24. Burton, R. (1982) Diagnosing bugs in a simple procedural skill. In Intelligent Tutoring Systems (eds D. H. Sleeman & J. S. Brown). Academic Press, London, pp. 157–183.Google Scholar
  25. Burton, R. & Brown, J. S. (1977) A tutoring and student modelling paradigm for gaming environments. SIGCSE Bulletin, 8, 236–246.Google Scholar
  26. Burton, R. & Brown, J. S. (1982) An investigation of computer coaching for informal learning activities. In Intelligent Tutoring Systems (eds D. H. Sleeman & J. S. Brown). Academic Press, London, pp. 79–98.Google Scholar
  27. Carbonell, J. R. (1970) AI in CAI: an artificial intelligence approach to computer-assisted instruction. IEEE Transactions on Man-Machine Systems, II, 190–202.Google Scholar
  28. Carbonell, J. R. (1971) Artificial intelligence and large interactive man computer systems. Proceedings of the Joint National Conference on Major Systems, Anahein, CA, pp. 167–173.Google Scholar
  29. Clancey, W. J. (1982) Tutoring rules for guiding a case method dialogue. In Intelligent Tutoring Systems (eds D. H. Sleeman & J. S. Brown). Academic Press, London, pp. 201–225.Google Scholar
  30. Clancey, W. J. (1983) GUIDON. Journal of Computer-Based Instruction, 10, 8–15.Google Scholar
  31. Clancey, W. J. (1984) Methodology for building an intelligent tutoring system. In Methods and Tactics in Cognitive Science (eds W. Kintsch, J. R. Miller & P. G. Polson). Lawrence Erlbaum, London.Google Scholar
  32. Clancey, W. J. (1987) Knowledge-Based Tutoring. MIT Press, London.Google Scholar
  33. Clancey, W. J. & Letsinger, R. (1981) NEOMYCIN: reconfiguring a rule-based expert system for application to teaching. Proceedings of the 7th International Joint Conference on Artificial Intelligence, Vancouver, Canada, pp. 829–835.Google Scholar
  34. Collins, A. & Stevens, A. L. (1982) Goals and strategies for inquiry teachers. In Advances in Instructional Psychology, Vol 2 (ed R. Glaser). Lawrence Erlbaum, Hillsdale, NJ.Google Scholar
  35. Crowder, N. A. (1959) Automatic tutoring by means of intrinsic programming. In Automatic Teaching: The State of the Art, Wiley, New York, pp. 109–116.Google Scholar
  36. Davies, N. G., Dickens, S. L. & Ford, L. (1985) Tutor — a prototype ICAI system. In Research and Development in Expert Systems (ed. M. A. Bramer). Cambridge University Press, Cambridge.Google Scholar
  37. Elsom-Cook, M. (1987) Intelligent Computer-aided instruction research at the Open University. Technical ReportNo: 63. Computer-Assisted Learning Research Group, The Open University, Milton Keynes.Google Scholar
  38. Genesereth, M. R. (1982) The role of plans in intelligent teaching systems. In Intelligent Tutoring Systems (eds D. H. Sleeman & J. S. Brown). Academic Press, London, pp. 137–155.Google Scholar
  39. Genter, D. R. (1977) The FLOW tutor: a schema-based tutorial system. Proceedings of the Fifth International Joint Conference on Artificial Intelligence, Cambridge, MA 787–790.Google Scholar
  40. Gilmore, D. & Self, J. A. (1988) The application of machine learning to intelligent tutoring systems. In Artificial Intelligence and Human Learning: Intelligent Computer-Aided Instruction (ed. J. A. Self). Chapman & Hall, London, pp. 179–196.Google Scholar
  41. Goldstein, I. P. (1982) The genetic graph: a representation for the evolution of procedural know-ledge. In Intelligent Tutoring Systems (eds D. H. Sleeman & J. S. Brown). Academic Press, London, pp. 51–77.Google Scholar
  42. Goldstein, I. P. & Miller, M. L. (1976) AI-based personal learning environments: directions for long term research. AI lab Memo384. Massachussetts Institute of Technology, Cambridge, MA.Google Scholar
  43. Grignetti, M., Hausman, C. L. & Gould, L. (1975) An intelligent on-line assistant and tutor: NLS-SCHOLAR. Proceedings of the National Computer Conference, 775–781.Google Scholar
  44. Hartley, J. R. & Sleeman, D. H. (1973) Towards more intelligent teaching systems. International Journal of Man-Machine Studies, 5, 215–236.CrossRefGoogle Scholar
  45. Hasemann, K. (1981) On difficulties with fractions. Educational Studies in Mathematics, 12, 71–287.Google Scholar
  46. Hawkes, W. L., Sharon, J. D., Kandel, A. & Taps Project Staff (1986) Fuzzy expert systems for an intelligent computer-based tutor. Technical Report No: 86-5. Learning Systems Institute, Centre for Educational Technology, Florida State University.Google Scholar
  47. Hollan, J. D., Hutchins, E. L. & Weitzman, L. (1984) STEAMER: an interactive inspectable simulation-based training system. AI Magazine, 5, 15–27.Google Scholar
  48. Joobbani, R. & Talukdar, S. N. (1985) An expert system for understanding expressions for electric circuits analysis. Proceedings of the Ninth International Joint Conference on Artificial Intelligence, Los Angeles, pp. 23–25.Google Scholar
  49. Kimball, R. A. (1982) A self-improving tutor for symbolic integration. In Intelligent Tutoring Systems (eds D. H. Sleeman & J. S. Brown). Academic Press, London, pp. 283–307.Google Scholar
  50. Koffman, E. B. & Blount, S. E. (1975) Artificial intelligence and automatic programming in CAI. Artificial Intelligence, 6, 215–234.Google Scholar
  51. Mandl, H. & Lesgold, A. (eds) (1988) Learning Issues for Intelligent Tutoring Systems. Springer-Verlag, London.Google Scholar
  52. McCalla, G. I., Greer, J. E. & SCENT Team (1988) Intelligent advising in problem solving domains: the SCENT-3 architecture. In Proceedings of the International Conference on Intelligent Tutoring Systems. Montreal, Canada, pp. 124–131.Google Scholar
  53. Murray, W. R. (1987) Automatic program debugging for intelligent tutoring systems. Computational Intelligence, 3(1).Google Scholar
  54. Nwana, H. S. (1989) An iterative-style approach to constructing intelligent tutoring systems in mathematics. PhD Thesis, Aston University, Birmingham.Google Scholar
  55. Nwana, H. S. (1990) The anatomy of FITS: a mathematic tutor. Intelligent Tutoring Media, 1(2).Google Scholar
  56. Ok-choon, P., Ray, S. P. & Seidel, R. J. (1987) Intelligent CAI: old winein new bottles or a new vintage? In Artificial Intelligence and Instruction: Instruction and Methods, Addison-Wesley, Reading, MA. pp. 11–43.Google Scholar
  57. O'Shea, T. (1982) A self-improving quadratic tutor. In Intelligent Tutoring Systems (eds D. H. Sleeman & J. S. Brown). Academic Press, London, pp. 283–307.Google Scholar
  58. O'Shea, T., Bornat, R., Du Boulay, B., Eisenstadt, M. & Page, I. (1984) Tools for creating intelligent computer tutors. In Artificial and Human Intelligence (eds ?. Elithorn & R. Beneiji). Elsevier, North Holland, pp. 181–199.Google Scholar
  59. O'Shea, R. & Self, J. (1983) Learning and Teaching with Computers. Harvester Press, Sussex.Google Scholar
  60. Papert, S. (1980) Mindstorms: Children, Computers and Powerful Ideas. Basic Books, New York.Google Scholar
  61. Peachey, D. R. & McCalla, G. I. (1986) Using planning techniques in intelligent tutoring systems. International Journal of Man-Machine Studies, 24, 77–98.Google Scholar
  62. Rich, E. (1979) User modelling via stereotypes. Cognitive Science, 3, 329–354.Google Scholar
  63. Ridgway, J. (1988) Of course ICAI is impossible...worse though, it might be seditious. In Artificial Intelligence and Human Learning: Intelligent computer-aided instruction (ed. J. A. Self). Chapman & Hall. London, pp. 28–48.Google Scholar
  64. Ross, P. (1987) Intelligent tutoring systems. Journal of Computer Assisted Learning, 3, 194–203.Google Scholar
  65. Ross, P., Jones, J. & Millington, P. (1987) User modelling in intelligent teaching and tutoring. In Trends in Computer Assisted Instruction (eds R. Lewis & E. D. Tagg). Blackwell, London, pp. 32–44.Google Scholar
  66. Self, J. A. (1974) Student models in computer-aided instruction. International Journal of Man-Machine Studies, 6, 261–276.Google Scholar
  67. Self, J. A. (1987a) The application of machine learning to student modelling. In Artificial Intelligence and Education 1: Learning Environments & Tutoring Systems (eds R. Lawler & M. Yazdani). Ablex, Norwood, pp. 267–280.Google Scholar
  68. Self, J. A. (1987b) Realism in student modelling. Alvey-IKBS Research Workshop Tutoring Systems. University of Exeter.Google Scholar
  69. Self, J. A. (ed.) (1988a) Artificial Intelligence and Human Learning: Intelligent computer-aided instruction. Chapman & Hall, London.Google Scholar
  70. Self, J. A. (1988b) Student models: what use are they? In Artificial Intelligence Tools in Education (eds P. Ercoli & R. Lewis). North Holland, Amsterdam, pp. 73–86.Google Scholar
  71. Shortliffe, E. H. (1976) Computer Based Medical Consultations: MYCIN. Elsevier, New York.Google Scholar
  72. Skinner, B. F. (1954) The science of learning and the art of teaching. Harvard Educational Review, 24, 86–97.Google Scholar
  73. Skinner, B. F. (1958) Teaching Machines. Science, 128, 969–977.Google Scholar
  74. Sleeman, D. H. (1975) A problem-solving monitor for a deductive reasoning task. International Journal of Man-Machine Studies, 7, 183–211.Google Scholar
  75. Sleeman, D. H. (1983) Intelligent tutoring systems: a review. Proceedings of EdCompCon '83 meeting. IEEE Computer Society, pp. 95–101.Google Scholar
  76. Sleeman, D. H. (1985) UMFE: a user modelling front-end subsystem. International Journal of Man-Machine Studies, 23, 71–88.Google Scholar
  77. Sleeman, D. H. (1987) PIXIE: a shell for developing intelligent tutoring systems. In Artificial Intelligence and Education (eds R. Lawler & M. Yazdani). Ablex, Norwood, pp. 239–265.Google Scholar
  78. Sleeman, D. H. & Brown, J. S. (eds) (1982a) Intelligent Tutoring Systems. Academic Press, London.Google Scholar
  79. Sleeman, D. H. & Brown, J. S. (1982b) Introduction: intelligent tutoring systems. In Intelligent Tutoring Systems (eds D. H. Sleeman & J. S. Brown). Academic Press, London, pp. 1–11.Google Scholar
  80. Sleeman, D. H. & Smith, M. J. (1981) Modelling students' problem solving. Artificial Intelligence, 16, 171–188.Google Scholar
  81. Soloway, E. & Johnson, W. (1984) Remembrance of blunders past: a retrospective on the development of PROUST. Proceedings of the Sixth Cognitive Science Society Conference. Boulder, CO. p. 57.Google Scholar
  82. Streitz, N. A. (1988) Mental, models and metaphors: implications for the design of adaptive user-system interfaces. In Learning Issues for Intelligent Tutoring Systems (eds H. Mandl & A. Lesgold). Springer-Verlag, London, pp. 164–186.Google Scholar
  83. Suppes, P. (1966) The uses of computers in education. Scientific American 25, 206–221.Google Scholar
  84. Suppes, P. (1967) Some theoretical models for mathematics learning. Journal of Research and Development in Education 1, 5–22.Google Scholar
  85. Tobias, S. (1985) Computer assisted instruction. In Adapting Instruction to Individual Differences (eds M. C. Wang & H. J. Waldberg). McCutchan, Berkeley, CA, pp. 139–159.Google Scholar
  86. Uhr, L. (1969) Teaching machine programs that generate problems as a function of interaction with students. Proceeding of the 24th National Conference. pp. 125–134.Google Scholar
  87. Vanlehn, K. (1987) Learning one subprocedure per lesson. Artificial Intelligence 31, 1–40.Google Scholar
  88. Wachsmuth, I. (1988) Modelling the knowledge base of mathematical learners: situation-specific and situation-nonspecific knowledge. In Learning Issues for Intelligent Tutoring Systems (eds H. Mandl & A. Lesgold). Springer-Verlag, London, pp. 63–79.Google Scholar
  89. Weischedel, R. M., Voge, W. M. & James, M. (1978) An artificial intelligence approach to language instruction. Artificial Intelligence 10, 225–240.Google Scholar
  90. Wenger, E. (1987) Artificial Intelligence and Tutoring Systems. Morgan Kaufmann, Los Altos, CA.Google Scholar
  91. Wexler, J. D. (1970) Information networks in generative computer-assisted instruction. IEEE Transactions on Man-Machine Systems 11, 181–190.Google Scholar
  92. White, B. Y. & Frederiksen, J. R. (1985) QUEST: qualitative understanding of electrical system troubleshooting. ACM SIGART Newsletter, 93, 34–37.Google Scholar
  93. Woods, P. & Hartley, J. R. (1971) Some learning models for arithmetic tasks and their use in computer-based learning. British Journal of Educational Psychology, 41, 35–48.Google Scholar
  94. Woolf, B. P. & McDonald, D. D. (1984) Context-dependent transitions in tutoring discourse. Proceedings of the National Conference on Artificial Intelligence. Austin, Texas, pp. 355–361.Google Scholar
  95. Yazdani, M. (1983) Introduction: artificial intelligence and education. In New Horizons in Educational Computing (ed. M. Yazdani). Wiley, New York.Google Scholar
  96. Yazdani, M. (1986) Intelligent tutoring systems survey. Artificial Intelligence Review, 1, 43–52.Google Scholar
  97. Yazdani, M. (1987) Intelligence tutoring systems: an overview. In Artificial Intelligence and Education (eds R. Lawler & M. Yazdani). Ablex, Norwood. pp. 182–201.Google Scholar
  98. Zissos, A. Y. & Witten, I. H. (1985) User modelling for a computer coach: a case study. International Journal of Man-Machine Studies, 23, 729–750.Google Scholar

Copyright information

© Inteliest Ltd 1990

Authors and Affiliations

  • Hyacinth S. Nwana
    • 1
  1. 1.Department of Computer ScienceUniversity of LiverpoolLiverpoolUK

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