Teaching Tactics and Dialog in AutoTutor

Abstract

The Tutoring Research Group at the University of Memphis has developed a computer tutor (called AutoTutor) that simulates the discourse patterns and pedagogical strategies of a typical human tutor. The dialog tactics were based on a previous project that dissected 100 hours of naturalistic tutoring sessions. AutoTutor is currently targeted for college students in introductory computer literacy courses, who learn the fundamentals of hardware, operating systems, and the Internet. Instead of merely being an information delivery system, AutoTutor serves as a discourse prosthesis (or collaborative scaffold) that assists the student in actively constructing knowledge. A dialog manager coordinates the conversation that occurs between a learner and a pedagogical agent, whereas lesson content and world knowledge are represented in a curriculum script and latent semantic analysis. The agent is a talking head with discourse- sensitive facial expressions and synthesized speech. Evaluations of AutoTutor have shown that the tutoring system improves learning and memory of the lessons by .5 to .6 standard deviation units. This article describes the components of AutoTutor and contrasts two versions that follow somewhat different teaching tactics.