A Conversation-Based Intelligent Tutoring System Benefits Adult Readers with Low Literacy Skills


This article introduces three distinctive features of a conversation-based intelligent tutoring system called AutoTutor. AutoTutor was designed to teach low literacy adult learners comprehension strategies across different levels of discourse processing. In AutoTutor, three-way conversations take place between two computers agents (a teacher agent and a peer agent) and a human learner. Computer agents scaffold learning by asking questions and providing feedback. The interface of AutoTutor is simple and easy to use and addresses the special technology needs of adult learners. One of AutoTutor’s strengths is that it is adaptive and as such can provide individualized instruction for the diverse population of adult literacy students. The adaptivity of AutoTutor is achieved by assessing learners’ performance and branching them into conditions with different difficulty level. Data from a reading comprehension intervention suggest that adult literacy students benefit from using AutoTutor. Such learning benefits may be increased by enhancing the adaptivity of AutoTutor. This may be accomplished by tailoring instruction and materials to meet the various needs of the individuals with low literacy skills.

Publication Title

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)