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Interactive Conceptual Tutoring in Atlas-Andes

AAAI Conferences

Atlas-Andes is a dialogue enhanced model tracing tutor (MTT) integrating the Andes Physics tutoring system (Gertner VanLelm 2000) with the Atlas tutorial dialogue system (Freedman et al. 2000). Andes is a MTT that presents quantitative physics problems to students. Each problem solving action entered by students is highlighted either red or green to indicate whether it. This basic feedback is terlned red-greeu feedback. Atlas provides Andes with the capability of leading students through directed lines of reasoning that teach basic physics conceptual knowledge, such as Newton's Laws.


Intelligent Tutoring Systems with Conversational Dialogue

AI Magazine

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 mixedinitiative 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.


Intelligent Tutoring Systems with Conversational Dialogue

AI Magazine

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.



Trialog: How Peer Collaboration Helps Remediate Errors in an ITS

AAAI Conferences

Many intelligent tutoring systems (ITSs) offer feedback and guidance through structured dialogs with their students, which often take the form of a sequence of hints. However, it is often difficult to replicate the complexity and responsiveness of human conversation with current natural language understanding and production technologies. Although ITSs reveal enough information to continue solving a problem, the conversations are not very engaging. To enhance engagement, the current study manipulated tutorial dialog by transforming them into a trialog by adding another student. Our intention was to advance the help offered by the system by putting students in a position to help each other, as well as make sense of the help offered by the ITS. The present paper attempts to show that conversations, either with the system or with a peer, are important design considerations when building an effective ITS.