Pedagogical Agent Design: The Impact of Agent Realism, Gender, Ethnicity, and Instructional Role

  • Amy L. Baylor
  • Yanghee Kim
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3220)


In the first of two experimental studies, 312 students were randomly assigned to one of 8 conditions, where agents differed by ethnicity (Black, White), gender (male, female), and image (realistic, cartoon), yet had identical messages and computer-generated voice. In the second study, 229 students were randomly assigned to one of 12 conditions where agents represented different instructional roles (expert, motivator, and mentor), also differing by ethnicity (Black, White), and gender (male, female). Overall, it was found that students had greater transfer of learning when the agents had more realistic images and when agents in the “expert” role were represented non-traditionally (as Black versus White). Results also generally confirmed prior research where agents perceived as less intelligent lead to significantly improved self-efficacy. The presence of motivational messages, as employed through the motivator and mentor agent roles, led to enhanced learner self-regulation and self-efficacy. Results are discussed with respect to social cognitive theory.


Efficacy Belief Female Agent Pedagogical Agent Agent Role Agent Realism 
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.


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  1. Arroyo, I., Beck, J.E., Woolf, B.P., Beal, C.R., Schultz, K.: Macroadapting animalwatch to gender and cognitive differences with respect to hint interactivity and symbolism. In: Gauthier, G., VanLehn, K., Frasson, C. (eds.) ITS 2000. LNCS, vol. 1839, pp. 574–583. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  2. Arroyo, I., Murray, T., Woolf, B.P., Beal, C.R.: Further results on gender and cognitive differences in help effectiveness. Paper presented at the The International Conference of Artificial Intelligence in Education, Sydney, Australia (2003)Google Scholar
  3. Bandura, A.: Self-efficacy: The exercise of control. W.H. Freeman, New York (1997)Google Scholar
  4. Bandura, A. (ed.): Self-Efficacy: The Foundation of Agency. Lawrence Erlbaum Associates, Inc., Mahwah (2000)Google Scholar
  5. Baylor, A.L.: Beyond butlers: intelligent agents as mentors. Journal of Educational Computing Research 22(4), 373–382 (2000)CrossRefGoogle Scholar
  6. Baylor, A.L.: Encouraging more positive engineering stereotypes with animated interface agents (2004) (unpublished manuscript) Google Scholar
  7. Baylor, A.L., Kim, Y.: The Role of Gender and Ethnicity in Pedagogical Agent Perception. In: Paper presented at the E-Learn (World Conference on E-Learning in Corporate, Government, Healthcare, & Higher Education), Phoenix, Arizona (2003a)Google Scholar
  8. Baylor, A.L., Kim, Y.: The role of gender and ethnicity in pedagogical agent perception. In: Paper presented at the E-Learn, the Annual Conference of Association for the Advancement of Computing in Education, Phoenix, AZ (2003b)Google Scholar
  9. Baylor, A.L., Kim, Y.: Validating Pedagogical Agent Roles: Expert, Motivator, and Mentor. Paper presented at the International Conference of Ed-Media, Honolulu, Hawaii (2003c)Google Scholar
  10. Baylor, A.L., Kim, Y.: The effectiveness of simulating instructional roles with pedagogical agents. International Journal of Artificial Intelligence in Education (in press)Google Scholar
  11. Baylor, A.L., Ryu, J.: The API (Agent Persona Instrument) for assessing pedagogical agent persona. In: Paper presented at the International Conference of Ed-Media, Honolulu, Hawaii (2003a)Google Scholar
  12. Baylor, A.L., Ryu, J.: Does the presence of image and animation enhance pedagogical agent persona? Journal of Educational Computing Research 28(4), 373–395 (2003b)CrossRefGoogle Scholar
  13. Baylor, A.L., Shen, E., Huang, X.: Which Pedagogical Agent do Learners Choose The Effects of Gender and Ethnicity. In: Paper presented at the E-Learn (World Conference on E-Learning in Corporate, Government, Healthcare, & Higher Education), Phoenix, Arizona (2003)Google Scholar
  14. Cassell, J.: A Framework For Gesture Generation And Interpretation. In: Pentland, A. (ed.) Computer Vision in Human-Machine Interaction, Cambridge University Press, New York (1998)Google Scholar
  15. Cooper, J., Weaver, K.D.: Gender and Computers: Understanding the Digital Divide. Lawrence Erlbaum Associates, NJ (2003)Google Scholar
  16. Driscoll, M.P.: Psychology of Learning for Instruction. Allyn & Bacon (2000)Google Scholar
  17. Dunn, J.: Mind-reading, emotion understanding, and relationships. International Journal of Behavioral Development 24(2), 142–144 (2000)CrossRefGoogle Scholar
  18. Ericsson, K.A.: The acquisition of expert performance: an introduction to some of the issues. In: Ericsson, K.A. (ed.) The Road to Excellence: The Acquisition of Expert Performance in the Arts, Sciences, Sports, and Games, pp. 1–50. Erlbaum, Hillsdale (1996)Google Scholar
  19. Ericsson, K.A., Krampe, R.T., Tesch-Romer, C.: The role of deliberate practice in the acquisition of expert performance. Psychological Review 100(3), 363–406 (1993)CrossRefGoogle Scholar
  20. Gonzales, M., Burdenski Jr., T.K., Stough, L.M., Palmer, D.J.: Identifying teacher expertise: an examination of researchers’ decision-making. Paper presented at the American Educational Research Association, Seattle, WA, April 10-14 (2001) Google Scholar
  21. Hays-Roth, B., Doyle, P.: Animate Characters. Autonomous Agents and Multi-Agent Systems 1, 195–230 (1998)CrossRefGoogle Scholar
  22. Johnson, W.L., Rickel, J.W., Lester, J.C.: Animated pedagogical agents: face-toface interaction in interactive learning environments. International Journal of Artificial Intelligence in Education 11, 47–78 (2000)Google Scholar
  23. Kim, Y., Baylor, A.L., Reed, G.: The Impact of Image and Voice with Pedagogical Agents. Paper presented at the E-Learn (World Conference on E-Learning in Corporate, Government, Healthcare, & Higher Education), Phoenix, Arizona (2003)Google Scholar
  24. Kort, B., Reilly, R., Picard, R.W.: An affective model of interplay between emotions and learning: reengineering educational pedagogy-building a learning companion. In: Proceedings IEEE International Conference on Advanced Learning Technologies, pp. 43–46 (2001)Google Scholar
  25. Lave, J., Wenger, E.: Situated learning: legitimate peripheral participation. Cambridge University Press, Cambridge (2001)Google Scholar
  26. Lee, E., Nass, C.: Does the ethnicity of a computer agent matter? An experimental comparison of human-computer interaction and computer-mediated communication. In: Paper presented at the WECC Conference, Lake Tahoe, CA (1998)Google Scholar
  27. McCrae, R.R., John, O.P.: An introduction to the fve factor model and its applications. Journal of Personality 60, 175–215 (1992)CrossRefGoogle Scholar
  28. McNeill, D.: Hand and mind: what gestures reveal about thought. University of Chicago Press, Chicago (1992)Google Scholar
  29. Moreno, K.N., Person, N.K., Adcock, A.B., Eck, R.N.V., Jackson, G.T., Marineau, J.C.: Etiquette and Efficacy in Animated pedagogical agents: the role of stereotypes. In: Paper presented at the AAAI Symposium on Personalized Agents, Cape Cod, MA (2002)Google Scholar
  30. Nass, C., Steuer, J.: Computers, voices, and sources of messages: computers are social actors. Human Communication Research 19(4), 504–527 (1993)CrossRefGoogle Scholar
  31. Norman, D.A.: How might people interact with agents? Communications of the ACM 37(7), 68–71 (1994)CrossRefGoogle Scholar
  32. Norman, D.A.: How might people interact with agents. In: Bradshaw, J.M. (ed.) Software agents, pp. 49–55. MIT Press, Menlo Park (1997)Google Scholar
  33. Passig, D., Levin, H.: Gender preferences for multimedia interfaces. Journal of Computer Assisted Learning 16(1), 64–71 (2000)CrossRefGoogle Scholar
  34. Persson, P., Laaksolahti, J., Lonnqvist, P.: Understanding social intelligence. In: Dautenhahn, K., Bond, A.H., Canamero, L., Edmonds, B. (eds.) Socially intelligent agents: Creating relationships with computers and robots, Kluwer Academic Publishers, Norwell (2002)Google Scholar
  35. Piaget, J.: Play, dreams, and imitation in childhood. Norton, New York (1962)Google Scholar
  36. Piaget, L.: Sociological studies (I. Smith, Trans. 2nd ed.). Routledge, New York (1995)Google Scholar
  37. Picard, R.: Affective Computing. The MIT Press, Cambridge (1997)Google Scholar
  38. Reeves, B., Nass, C.: The Media Equation: How people treat computers, television, and new media like real people and places. Cambridge University Press, Cambridge (1996)Google Scholar
  39. Rizzo, P.: Why should agents be emotional for entertaining users? A critical analysis. In: Paiva, A.M. (ed.) Affective interaction: Towards a new generation of computer interfaces, pp. 166–181. Springer, Berlin (2000)Google Scholar
  40. Roth, W.-M.: Gestures: their role in teaching and learning. Review of Educational Research 71(3), 365–392 (2001)CrossRefGoogle Scholar
  41. Saarni, C.: Emotion communication and relationship context. International Journal of Behavioral Development 25(4), 354–356 (2001)CrossRefGoogle Scholar
  42. Schunk, D.H.: Social cognitive theory and self-regulated learning. In: Zimmerman, B.J., Schunk, D.H. (eds.) Self-regulated learning and academic achievement: Theory, research, and practice, pp. 83–110. Springer, New York (1989)Google Scholar
  43. Vassileva, J.: Goal-based autonomous social agents: Supporting adaptation and teaching in a distributed environment. In: Paper presented at the 4th International Conference of ITS 1998, San Antonio, TX (1998)Google Scholar
  44. Vygotsky, L.S., Cole, M., John-Steiner, V., Scribner, S., Souberman, E.: Mind in society. Harvard University Press, Cambridge (1978)Google Scholar
  45. Zimmerman, B.J.: Attaining self-regulation: A social cognitive perspective. In: Boekaerts, M., Pintrich, P., Zeidner, M. (eds.) Self-Regulation: Theory, Research and Application, pp. 13–39. Academic Press, Orlando (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Amy L. Baylor
    • 1
  • Yanghee Kim
    • 1
  1. 1.Pedagogical Agent Learning Systems (PALS) Research Laboratory, Department of Educational Psychology and Learning SystemsFlorida State UniversityUnited States

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