Intelligent tutoring systems with conversational dialogue

TLDR

Intelligent tutoring systems developed over the past two decades have shown success, especially in math, science, and technology, by enabling students to actively construct knowledge through conversational interaction rather than passive information delivery. The authors aim to present a new generation of intelligent tutoring systems that engage learners in mixed‑initiative conversational dialogues. These systems, such as AutoTutor with a talking head for computer literacy and Andes, Atlas, and Why2 for physics, present challenging problems and sustain lengthy multiturn English dialogues where learners type answers and solutions evolve. They achieve significant learning gains outside classrooms and sustain students' attention and interest for hours.

Abstract

Many of the intelligent tutoring systems that have been developed during the last 20 years have proven to be quite successful, particularly in the domains of mathematics, science, and technology. They produce significant learning gains beyond classroom environments. They are capable of engaging most students' attention and interest for hours. We have been working on a new generation of intelligent tutoring systems that hold mixed-initiative conversational dialogues with the learner. The tutoring systems present challenging problems and questions to the learner, the learner types in answers in English, and there is a lengthy multiturn dialogue as complete solutions or answers evolve. This article presents the tutoring systems that we have been developing. AutoTutor is a conversational agent, with a talking head, that helps college students learn about computer literacy. andes, atlas, and why2 help adults learn about physics. Instead of being mere information-delivery systems, our systems help students actively construct knowledge through conversations.