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AutoTutor is an intelligent tutoring system developed by researchers at the Institute for Intelligent Systems at the University of Memphis, including Arthur C.
Graesser that helps students learn Newtonian physics, computer literacy, and critical thinking topics through tutorial dialogue in natural language.
AutoTutor differs from other popular intelligent tutoring systems such as the Cognitive Tutor, in that it focuses on natural language dialog.
This means that the tutoring occurs in the form of an ongoing conversation, with human input presented using either voice or free text input.
To handle this input, AutoTutor uses computational linguistics algorithms including latent semantic analysis, regular expression matching, and speech act classifiers.
These complementary techniques focus on the general meaning of the input, precise phrasing or keywords, and functional purpose of the expression, respectively.
In addition to natural language input, AutoTutor can also accept ad hoc events such as mouse clicks, learner emotions inferred from emotion sensors, and estimates of prior knowledge from a student model.
Based on these inputs, the computer tutor determine when to reply and what speech acts to reply with.
This process is driven by a script that includes a set of dialog-specific production rules.
