Abstract
This poster describes Rimac, a natural-language tutoring system that engages students in dialogues that address physics concepts and principles, after students have solved quantitative physics problems. We summarize our approach to deriving decision rules that simulate the highly interactive nature of human tutoring, and describe a pilot test that compares two versions of Rimac: an experimental version that deliberately executes these decision rules within a Knowledge Construction Dialogue (KCD) framework, and a control KCD system that does not intentionally execute these rules.
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Katz, S. et al. (2013). Pilot Test of a Natural-Language Tutoring System for Physics That Simulates the Highly Interactive Nature of Human Tutoring. In: Lane, H.C., Yacef, K., Mostow, J., Pavlik, P. (eds) Artificial Intelligence in Education. AIED 2013. Lecture Notes in Computer Science(), vol 7926. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39112-5_77
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DOI: https://doi.org/10.1007/978-3-642-39112-5_77
Publisher Name: Springer, Berlin, Heidelberg
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