Intelligent Tutoring Systems
  Home     Contact     Syllabus     Outline     Projects  

Fall 2007 Outline

This class is being developed based on the course User-Adaptive Systems and Intelligent Learning Environments by Cristina Conati at UBC in Winter 2003-2004, and on the Intelligent Tutoring Systems course taught by Neil Heffernan

DateTopic(s)Reading Assignment
8/20/07Introduction AAAI's introduction to Intelligent Tutoring Systems. In particular:
  • Eric Thomas's brief intro to Intelligent Tutoring Systems
  • Applications of AI in Education By Joseph Beck, Mia Stern, and Erik Haugsjaa. ACM Crossroads (student magazine of the Association for Computing Machinery), 1996.
  • See Bloom for a discussion of tutoring as the most effective teaching method:
    • Bloom, BS, "The 2 Sigma problem: The Search for Methods of Group Instruction as Effective as One-to-One Tutoring" [1984] Educational Researcher 4-16.
  • How to Read a Research Paper, by Cristina Conati, 2000. Online:
8/27/07 Adaptive Support to Learning: Model Tracing Coaches
9/10/07 Educational Data Mining Methods for Knowledge and Model Tracing
9/17/07 Using Think-Alouds to understand student thinking Optional Further Background Reading:
  • Gertner, A., Conati, C., & VanLehn, K. (1998). Procedural help in Andes: Generating hints using a Bayesian network student model (pp. 106-111), In Proceedings of the 15th national Conference on Artificial Intelligence, Cambridge, MA: The MIT Press. [8 pgs.]
  • Newell, A., & Simon, H. A. (1972). Human problem solving. Englewood Cliffs, NJ: Prentice-Hall.
  • Cristina Conati, Abigail S. Gertner, Kurt VanLehn, Marek J. Druzdzel (1997) On-Line Student Modeling for Coached Problem Solving Using Bayesian Networks In Anthony Jameson, Cécile Paris, and Carlo Tasso (Eds.), User Modeling: Proceedings of the Sixth International Conference,UM97. Vienna, New York: Springer Wien New York. © CISM, 1997. Available online from, or
  • Chabay, R. W., & Sherwood, B. A. (1992). A practical guide for the creation of educational software. In Larkin, J. H. and Chabay, R. W. (Eds.), Computer-Assisted Instruction and Intelligent Tutoring Systems: Shared Goals and Complementary Approaches. Hillsdale, NJ: Lawrence Erlbaum Associates.
  • Shute, V. J., & Psotka, J. (1996). Intelligent tutoring systems: Past, Present and Future. In D. Jonassen (Ed.), Handbook of Research on Educational Communications and Technology : Scholastic Publications. [80 pages] - For a wider approach to ITS. See Slides by Cristina Conati.
  • Eberts, R. E. (1997). Computer-based instruction. In Helander, M. G., Landauer, T. K., & Prabhu, P. V. (Ed.s) Handbook of Human-Computer Interaction, (pp. 825-847). Amsterdam, The Netherlands: Elsevier Science B. V.
Seminal Books:
  • Wenger, E. (1987). Artificial intelligence and tutoring systems: Computational and cognitive approaches to the communication of knowledge. Los Altos, CA: Morgan Kaufmann Publishers. Especially Chapter 1 and 2.
  • Sleeman, D. H., & Brown, J. S. (1982). Intelligent Tutoring Systems. New York, NY: Academic Press.
  • Donovan, M. S., Bransford, J. D., & Pellegrino, J.W. (1999). How people learn: Bridging research and practice. Washington, D.C.: National Academy Press. Chapter 1, 10 and 11.
  • 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. [13 pgs.]
09/24/07Historical Background & Cognitive Modeling
  • VanLehn, K. (2006). The Behavior of Tutoring Systems. International Journal of Artificial Intelligence in Education. 16, pages not determined yet.

  • du Boulay, Benedict (2006) Commentary on Kurt VanLehn's The behaviour of tutoring systems. International Journal of Artificial Intelligence in Education, 16 (3). pp. 267-270.

  • Burton, R. R., & Brown, J. S. (1982). An investigation of computer coaching for informal learning activities (pp. 79-98). In D. Sleeman and J. S. Brown (Ed.), Intelligent Tutoring Systems. New York: Academic Press. [20 pgs.] 

  • VanLehn, K., Lynch, C., Schulze, K., Shapiro, J.A., Shelby, R.,Taylor, L., Treacy, D., Weinstein, A., and Wintersgill, M. (2005).  The Andes Physics Tutoring System: Lessons Learned.   International Journal of Artificial Intelligence and Education, 15 (3).
  • Ritter, F. & Feurzeig, W. (1988). Teaching real-time tactical thinking (pp. 285-301). In J. Psotka, L. D. Massey & S. A. Mutter (Eds.) Intelligent Tutoring Systems: Lessons Learned. Hillsdale, NJ: Erlbaum.
  • Project Slides
  • 10/1/07
    Cognitive Modeling
    • Anderson,J. R. (1993). Rules of the mind. Hillsdale, NJ.
      Parts to read: Chapter 1 (pages 1-10) “Production Systems and the ACT-R Theory” and Chapter 2 (pages 17-20, 25-44) Knowledge Representation.
    Cognitive Modeling: Learning & Transfer
    ITS Design Principles
    • Koedinger, K. R. (2002). Toward evidence for instructional design principles: Examples from Cognitive Tutor Math 6. Invited paper in Proceedings of PME-NA XXXIII (the North American Chapter of the International Group for the Psychology of Mathematics Education).
    • Merrill, D. C., & Reiser, B. J. (1994). Scaffolding effective problem solving strategies in interactive learning environments. In A. Ram & K. Eiselt (Eds.), Proceedings of the Sixteenth Annual Conference of the Cognitive Science Society (pp. 629-634). Hillsdale, NJ: Erlbaum. [5 pgs.][Handed out in class]
    • Dugdale (1992). In Larkin, J. H. and Chabay, R. W. (Eds.), Computer-Assisted Instruction and Intelligent Tutoring Systems: Shared Goals and Complementary Approaches. Hillsdale, NJ: Lawrence Erlbaum Associates.
    • Chabay, R. W., & Sherwood, B. A. (1992). A practical guide for the creation of educational software. In Larkin, J. H. and Chabay, R. W. (Eds.), Computer-Assisted Instruction and Intelligent Tutoring Systems: Shared Goals and Complementary Approaches. Hillsdale, NJ: Lawrence Erlbaum Associates.
    • Merrill, D. C. & Reiser, B. J. (1993). Scaffolding the acquisition of complex skills with reasoning-congruent learning environments. In Proceedings of the Workshop in Graphical Representations, Reasoning and Communication from the World Conference on Artificial Intelligence in Education. 9-15. Edinburgh, Scotland: The University of Edinburgh.
    Cognitive Mastery Learning and Knowledge Tracing Optional additional reading:
    Difficulty Factor Assessments
    Constraint Based Modeling Optional:
  • Suraweera, P. & Mitrovic, A. (2002) KERMIT: a Constraint-based Tutor for Database Modeling. in Intelligent Tutoring Systems 2002
  • 11/19/07Tutor Authoring
    • Murray, T. (1999). Authoring intelligent tutoring systems: An analysis of the state of the art. International Journal of Artificial Intelligence in Education, 10, pp. 98-129. (right-click and "save target to" disk)'s%20ReviewPaper.pdf
    • Moreno, R., & Mayer, R. E. (2007). Interactive multimodal learning environments. Educational Psychology Review, 19, 309-326. Get it by searching for the EPR at
    • Moreno, R. (2006). Does the modality principle hold for different media? A test of the method affects learning hypothesis. Journal of Computer Assisted Learning. Get it by searching for the JCAL at
    • Munro, A. (1994). RIDES Authoring Reference. Behavioral Technology Laboratories, USC (
    12/3/07 Learning Styles & HCI Discussion questions:
    • Compare this learning style system to the ones in the Hermann and 4MAT models.
    • How do we incorporate material for each kind of learner in an ITS?
    • What are some ways we can adapt to learners in ITSs?
    • How do we incorporate Norman's Design principles into ITS design?
    Optional readings:
    • Howard Gardner, Multiple Intelligences: The Theory in Practice. Chapter 1. New York: Basic Books, 1993.
    • Robert Ennis, "A Taxonomy of Critical Thinking Dispositions and Abilities," in Teaching Thinking Skills. Robert Stermberg, ed. New York: WH Freeman and Company, 1987.
    • Bernice McCarthy, The 4MAT System. Barrington, IL: EXCEL, Inc., 1987.
    12/10/07Final Exam
    TBD Adaptive Support to Learning: Student Modeling
    • VanLehn. K.(1988). Student Modeling. In M.C. Polson and J.J Richardson, Foundations of Intelligent Tutoring Systems, (pp55-78).
    • Self. , J. Bypassing the intractable problem of student modeling. In C. Frasson and G. Gauthier (eds.). Intelligent tutoring systems: at the crossroad of artificial intelligence and education (pp.107-123). Norwood, NJ: Ablex Publishing Corporation, Barnes slides
    • Conati C., Gertner A., VanLehn K. (2002). Using Bayesian Networks to Manage Uncertainty in Student Modeling. User Modeling and User-Adapted Interaction. 12(4), Conati Slides, Barnes Slides
    TBDPedagogical Agents & JESS