AUTOTUTOR IN THE CLOUD: A SERVICE-ORIENTED PARADIGM FOR AN INTEROPERABLE NATURAL-LANGUAGE ITS

Benjamin NYE, Xiangen HU, Arthur GRAESSER, Zhiqiang CAI

Abstract


Artificial intelligence has been used to power conversational tutoring systems since the early days of the field. Despite this longstanding research focus, scalable conversational intelligent tutoring systems have encountered significant challenges for both tutoring delivery and developing conversational tutors. AutoTutor, a longstanding intelligent tutoring system (ITS) project for natural language tutoring, has approached these challenges. This paper describes three facets of AutoTutor services: the AutoTutor Conversation Engine (ACE), AutoTutor Authoring Tools (ASAT), and the Sharable Knowledge Objects (SKO) framework. This framework combines intelligent tutoring, semantic analysis, and service-oriented patterns to provide natural language computer tutoring. Compared to existing natural language tutoring systems, this design pushes the bounds for delivering conversational tutoring as a service, authoring natural-language tutoring scripts, service-oriented semantic messaging with roots in the Experience API (xAPI) and Foundations for Intelligent Physical Agents (FIPA) standards, and integrating with other systems such as virtual worlds and web clients. Innovations in each of these areas will be reviewed briefly. Integration and scalability challenges are also discussed.

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References


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