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Evolution of user interaction: the case of agent adele
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Source International Conference on Intelligent User Interfaces archive
Proceedings of the 8th international conference on Intelligent user interfaces table of contents
Miami, Florida, USA
SESSION: Full Technical Papers table of contents
Pages: 93 - 100  
Year of Publication: 2003
ISBN:1-58113-586-6
Authors
W. Lewis Johnson  University of Southern California, Marina del Rey, CA
Erin Shaw  University of Southern California, Marina del Rey, CA
Andrew Marshall  University of Southern California, Marina del Rey, CA
Catherine LaBore  University of Southern California, Marina del Rey, CA
Sponsors
ACM: Association for Computing Machinery
SIGART: ACM Special Interest Group on Artificial Intelligence
SIGCHI: ACM Special Interest Group on Computer-Human Interaction
Publisher
ACM  New York, NY, USA
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Downloads (6 Weeks): 5,   Downloads (12 Months): 38,   Citation Count: 7
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ABSTRACT

Animated pedagogical agents offer promise as a means of making computer-aided learning more engaging and effective. To achieve this, an agent must be able to interact with the learner in a manner that appears believable, and that furthers the pedagogical goals of the learning environment. In this paper we describe how the user interaction model of one pedagogical agent evolved through an iterative process of design and user testing. The pedagogical agent Adele assists students as they assess and diagnose medical and dental patients in clinical settings. We describe the results of, and our responses to, three studies of Adele, involving over two hundred and fifty medical and dental students over five years, that have led to an improved tutoring strategy, and discuss the interaction possibilities of two different reasoning engines. With the benefit of hindsight, the paper articulates the principles that govern effective user-agent interaction in educational contexts, and describes how the agents interaction design in its current form embodies those principles


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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Clancey, W. J. & Letsinger, R. (1984). NEOMYCIN: Reconfiguring a Rule-Based Expert System for Application to Teaching, In W.J. Clancey & E. H. Shortliffe (Eds.), Readings in Medical Artificial Intelligence: The First Decade. Reading, MA, Addison-Wesley.
 
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Horvitz, E., Breese, J., Hecherman, D., Hovel, D., Rommelse, K. (1998). The Lumière Project: Bayesian User Modeling for Inferring the Goals and Needs of Software Users. Proc. of the 14th. Conf. on Uncertainty in AI.
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Johnson, W.L., Rickel, J., and Lester, J. (2000). Animated Pedagogical Agents: Face-to-Face Interaction in Interactive Learning Environments, International Journal of Artificial Intelligence in Education 11, 47--78.
 
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Johnson, W.L., Narayanan, S., Whitney, R., Das, R., Bulit, M., & LaBore, C. (2002). Limited domain synthesis of expressive military speech for animated characters. IEEE TTS Workshop.
 
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Rickel, J. and Johnson, W.L. (1999). Animated agents for procedural training in virtual reality: perception, cognition, and motor control, Applied Artificial Intelligence Journal, Vol. 13, 343--382, 1999.
 
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Sansone, C. and Harackiewicz, J.M. (2000). Intrinsic and extrinsic motivation: The search for optimal motivation and performance. San Diego: Academic Press.
 
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Shaw, E., Ganeshan, R., Johnson, W. L., and Millar, D. (1999). Building a Case for Agent-Assisted Learning as a Catalyst for Curriculum Reform in Medical Education, Proceedings of AIED '99, 509--516. Amsterdam: IOS Press.
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CITED BY  7

Collaborative Colleagues:
W. Lewis Johnson: colleagues
Erin Shaw: colleagues
Andrew Marshall: colleagues
Catherine LaBore: colleagues