The foundations and architecture of autotutor
Abstract
The Tutoring Research Group at the University of Memphis is developing an intelligent tutoring system which takes advantages of recent technological advances in the areas of semantic processing of natural language, world knowledge representation, multimedia interfaces, and fuzzy descriptions. The tutoring interaction is based on in-depth studies of human tutors, both skilled and unskilled. Latent semantic analysis will be used to semantically process and provide a representation for the student’s contributions. Fuzzy production rules select appropriate topics and tutor dialogue moves from a rich curriculum script. The production rules will implement a variety of different tutoring styles, from a basic untrained tutor to one which uses sophisticated pedagogical strategies. The tutor will be evaluated on the naturalness of its interaction, with Turing-style tests, by comparing different tutoring styles, and by judging learning outcomes.
Publication Title
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Recommended Citation
Wiemer-Hastings, P., Graesser, A., Franklin, S., Garzon, M., Gholson, B., Hacker, D., Hu, X., & Kreuz, R. (1998). The foundations and architecture of autotutor. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 1452, 334-343. https://doi.org/10.1007/3-540-68716-5_39
