AutoTutor: a tutor with dialogue in natural language

Arthur C Graesser, Shulan Lu, George Tanner Jackson, Heather Hite Mitchell, Mathew Ventura, Andrew Olney, Max M Louwerse

Research output: Contribution to journalArticleScientificpeer-review

284 Citations (Scopus)

Abstract

AutoTutor is a learning environment that tutors students by holding a conversation in natural language. AutoTutor has been developed for Newtonian qualitative physics and computer literacy. Its design was inspired by explanation-based constructivist theories of learning, intelligent tutoring systems that adaptively respond to student knowledge, and empirical research on dialogue patterns in tutorial discourse. AutoTutor presents challenging problems (formulated as questions) from a curriculum script and then engages in mixed initiative dialogue that guides the student in building an answer. It provides the student with positive, neutral, or negative feedback on the student's typed responses, pumps the student for more information, prompts the student to fill in missing words, gives hints, fills in missing information with assertions, identifies and corrects erroneous ideas, answers the student's questions, and summarizes answers. AutoTutor has produced learning gains of approximately .70 sigma for deep levels of comprehension.

Original languageEnglish
Pages (from-to)180-192
Number of pages13
JournalBehavior Research Methods, Instruments & Computers
Volume36
Issue number2
Publication statusPublished - 2004
Externally publishedYes

Keywords

  • Algorithms
  • Artificial Intelligence
  • Computer-Assisted Instruction
  • Humans
  • Natural Language Processing
  • Problem-Based Learning
  • Program Evaluation
  • Students
  • Teaching
  • User-Computer Interface

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