Educational Technology Research and Development

, Volume 58, Issue 6, pp 649–669 | Cite as

Preparing students for future learning with Teachable Agents

  • Doris B. ChinEmail author
  • Ilsa M. Dohmen
  • Britte H. Cheng
  • Marily A. Oppezzo
  • Catherine C. Chase
  • Daniel L. Schwartz
Research Article


One valuable goal of instructional technologies in K-12 education is to prepare students for future learning. Two classroom studies examined whether Teachable Agents (TA) achieves this goal. TA is an instructional technology that draws on the social metaphor of teaching a computer agent to help students learn. Students teach their agent by creating concept maps. Artificial intelligence enables TA to use the concept maps to answer questions, thereby providing interactivity, a model of thinking, and feedback. Elementary schoolchildren learning science with TA exhibited “added-value” learning that did not adversely affect the “basic-value” they gained from their regular curriculum, despite trade-offs in instructional time. Moreover, TA prepared students to learn new science content from their regular lessons, even when they were no longer using the software.


Instructional technology Learning-by-teaching Concept mapping Preparation for future learning (PFL) Science education Transfer 



This material is based upon work supported by the Institute of Education Sciences within the U.S. Department of Education under Award No. R305H060089, and the National Science Foundation under Grant Nos. 0634044 and 0904324. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the granting agencies.


  1. Annis, L. (1983). The processes and effects of peer tutoring. Human Learning, 2, 39–47.Google Scholar
  2. Bargh, J. A., & Schul, Y. (1980). On the cognitive benefits of teaching. Journal of Educational Psychology, 72, 593–604.CrossRefGoogle Scholar
  3. Barron, B., Martin, C. K., Takeuchi, L., & Fithian, R. (2009). Parents as learning partners in the development of technological fluency. International Journal of Learning and Media, 1(2), 55–77.CrossRefGoogle Scholar
  4. Baylor, A. L. (2007). Pedagogical agents as a social interface. Educational Technology, 47(1), 11–14.Google Scholar
  5. Biswas, G., Schwartz, D. L., Bransford, J. D., & The Teachable Agents Group at Vanderbilt. (2001). Technology support for complex problem solving: From SAD environments to AI. In K. Forbus & P. Feltovich (Eds.), Smart machines in education (pp. 71–98). Menlo Park, CA: AAAI/MIT Press.Google Scholar
  6. Biswas, G., Leelawong, K., Schwartz, D. L., Vye, N., & TAG-V. (2005). Learning by teaching: A new agent paradigm for educational software. Applied Artificial Intelligence, 19, 363–392.Google Scholar
  7. Bransford, J. D., & Schwartz, D. L. (1999). Rethinking transfer: A simple proposal with multiple implications. In A. Iran-Nejad & P. D. Pearson (Eds.), Review of research in education (Vol. 24, pp. 61–101). Washington, DC: American Educational Research Association.Google Scholar
  8. Burnstein, R. A., & Lederman, L. M. (2001). Using wireless keypads in lecture classes. The Physics Teacher, 39, 8–11.CrossRefGoogle Scholar
  9. Chase, C. C., Chin, D. B., Oppezzo, M. A., & Schwartz, D. L. (2009). Teachable agents and the protégé effect: Increasing the effort towards learning. Journal of Science Education and Technology, 18(4), 334–352.CrossRefGoogle Scholar
  10. Chi, M., Siler, S., Jeong, H., Yamauchi, T., & Hausmann, R. G. (2001). Learning from human tutoring. Cognitive Science, 25, 471–533.CrossRefGoogle Scholar
  11. Clarke, J., & Dede, C. (2009). Robust designs for scalability. In L. Moller, J. B. Huett, & D. M. Harvey (Eds.), Learning and instructional technologies for the 21st century: Visions of the future (pp. 27–48). New York: Springer.Google Scholar
  12. Collins, A., Brown, J. S., & Holum, A. (1991). Cognitive apprenticeship: Making thinking visible. American Educator, 15(3), 6–11. 38-46.Google Scholar
  13. Ellington, A. J. (2003). A meta-analysis of the effects of calculators on students’ achievement and attitude levels in precollege mathematics classes. Journal for Research in Mathematics Education, 34(5), 433–463.CrossRefGoogle Scholar
  14. Forbus, K. (1984). Qualitative process theory. Artificial Intelligence, 24(1–3), 85–168.CrossRefGoogle Scholar
  15. Galletta, D. F., Durcikova, A., Everard, A., & Jones, B. (2005). Does spell-checking software need a warning label? Communications of the ACM, 48(7), 82–85.CrossRefGoogle Scholar
  16. Gee, J. P. (2003). What video games have to teach us about learning and literacy. New York: Palgrave/Macmillan.Google Scholar
  17. Gopnik, A., & Schulz, L. (Eds.). (2007). Causal learning: Psychology, philosophy, computation. New York: Oxford University Press.Google Scholar
  18. Hacker, D. J., Dunlosky, J., & Graesser, A. C. (Eds.). (2009). Handbook of metacognition in education. New York: Routledge, Taylor & Francis.Google Scholar
  19. Hilbert, T. S., & Renkl, A. (2008). Concept mapping as a follow-up strategy to learning from texts: What characterizes good and poor mappers? Instructional Science, 36, 53–73.CrossRefGoogle Scholar
  20. Horton, P. B., McConney, A. A., Gallo, M., Woods, A. L., Senn, G. J., & Hamelin, D. (1993). An investigation of the effectiveness of concept mapping as an instructional tool. Science Education, 77(1), 95–111.CrossRefGoogle Scholar
  21. Ito, M. (2009). Hanging out, messing around, geeking out: Kids living and learning with new media. Cambridge, MA: MIT Press.Google Scholar
  22. Jackson, S. L., Krajcik, J., & Elliot, S. (1998). The design of guided learner-adaptable scaffolding in interactive learning environments. In The proceedings of CHI (pp. 187–194). NY: ACM Publishers.Google Scholar
  23. Judson, E., & Sawada, D. (2002). Learning from past and present: Electronic response systems in college lecture halls. Journal of Computers in Mathematics and Science Teaching, 21(2), 167–181.Google Scholar
  24. Kuhl, P. K., Tsao, F. M., & Liu, H. M. (2003). Foreign-language experience in infancy: Effects of short-term exposure and social interaction on phonetic learning. Proceedings of the National Academy of Science, 100, 9096–9101.CrossRefGoogle Scholar
  25. Martin, L., & Schwartz, D. L. (2009). Prospective adaptation in the use of representational tools. Cognition and Instruction, 27(04), 1–31.Google Scholar
  26. Nesbit, J. C., & Adesope, O. O. (2006). Learning with concept and knowledge maps: A meta-analysis. Review of Educational Research, 76(3), 413–448.CrossRefGoogle Scholar
  27. Nisbett, R. E., Krantz, D., Jepson, C., & Kunda, Z. (1983). The use of statistical heuristics in everyday inductive reasoning. Psychological Review, 90, 339–363.CrossRefGoogle Scholar
  28. Novak, J. D. (2002). Meaningful learning: The essential factor for conceptual change in limited or inappropriate propositional hierarchies leading to empowerment of learners. Science Education, 86, 548–571.CrossRefGoogle Scholar
  29. Novak, J. D., & Gowin, D. B. (1984). Learning how to learn. New York: Cambridge University Press.Google Scholar
  30. O’Donnell, A. M., Dansereau, D. F., & Hall, R. H. (2002). Knowledge maps as scaffolds for cognitive processing. Educational Psychology Review, 14(1), 71–86.CrossRefGoogle Scholar
  31. Palincsar, A., & Brown, A. (1984). Reciprocal teaching of comprehension-fostering and comprehension monitoring activities. Cognition and Instruction, 1(2), 117–175.CrossRefGoogle Scholar
  32. Renkl, A. (1995). Learning for later teaching: An exploration of mediational links between teaching expectancy and learning results. Learning and Instruction, 5, 21–36.CrossRefGoogle Scholar
  33. Roscoe, R. D., & Chi, M. (2008). Tutor learning: The role of explaining and responding to questions. Instructional Science, 36, 321–350.CrossRefGoogle Scholar
  34. Ruiz-Primo, M. A., & Shavelson, R. J. (1996). Problems and issues in the use of concept maps in science assessment. Journal of Research in Science Teaching, 33, 569–600.CrossRefGoogle Scholar
  35. Schwartz, D. L., Chase, C, Chin, D. B., Oppezzo, M., Kwong, H., Okita, S., et al. (2009). Interactive metacognition: Monitoring and regulating a teachable agent. In D. J. Hacker, J. Dunlosky, & A. C. Graesser (Eds.), Handbook of metacognition in education (pp. 340–358). New York: Routledge, Taylor & Francis.Google Scholar
  36. Stevens, R., Satwicz, T., & McCarthy, L. (2008). In-game, in-room, in-world: Reconnecting video game play to the rest of kids’ lives. In K. Salen (Ed.), The ecology of games: Connecting youth, games, and learning (pp. 41–66). Cambridge, MA: MIT Press.Google Scholar
  37. Taricani, E. M., & Clariana, R. B. (2006). A technique for automatically scoring open-ended concept maps. Educational Technology Research & Design, 54(1), 65–82.CrossRefGoogle Scholar
  38. Uretsi, J. A. R. (2000). Should I teach my computer peer? Some issues in teaching a learning companion. In G. Gautheir, C. Frasson, & K. VanLehn (Eds.), Intelligent tutoring systems (pp. 103–112). Berlin: Springer-Verlag.Google Scholar

Copyright information

© Association for Educational Communications and Technology 2010

Authors and Affiliations

  • Doris B. Chin
    • 1
    Email author
  • Ilsa M. Dohmen
    • 1
  • Britte H. Cheng
    • 3
  • Marily A. Oppezzo
    • 2
  • Catherine C. Chase
    • 2
  • Daniel L. Schwartz
    • 2
  1. 1.Stanford Center for Innovations in LearningStanford UniversityStanfordUSA
  2. 2.School of EducationStanford UniversityStanfordUSA
  3. 3.Center for Technology in LearningSRI InternationalMenlo ParkUSA

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