To handle this input, AutoTutor uses computational linguistics algorithms including latent semantic analysis, regular expression matching, and speech act classifiers.
It engages in a collaborative, mixed initiative dialog while constructing the answer, a process that typically takes approximately 100 conversational turns.
AutoTutor speaks the content of its turns through an animated conversational agent with a speech engine, some facial expressions, and rudimentary gestures.
[5] AutoTutor has shown learning gains, particularly on deep reasoning questions, in over a dozen experiments on college students for topics in introductory computer literacy[6] and conceptual physics.
However, the time and cost of authoring content is significantly greater than non-interactive educational materials such as slide decks or traditional textbooks, which is a common problem for intelligent tutoring systems.