AutoTutor: a tutor with dialogue in natural language.
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.
Graesser
Arthur C
AC
Department of Psychology, University of Memphis, Tennessee 38152-3230, USA. a-graesser@memphis.edu
Lu
Shulan
S
Jackson
George Tanner
GT
Mitchell
Heather Hite
HH
Ventura
Mathew
M
Olney
Andrew
A
Louwerse
Max M
MM
D000465:Algorithms; D001185:Artificial Intelligence; D003194:Computer-Assisted Instruction; D006801:Humans; D009323:Natural Language Processing; D018794:Problem-Based Learning / Q000379:methods*; D015397:Program Evaluation; D013334:Students; D013663:Teaching / Q000379:methods*; D014584:User-Computer Interface