G. Tanner Jackson

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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(More)
It is often assumed that engaging in a one-on-one dialogue with a tutor is more effective than listening to a lecture or reading a text. Although earlier experiments have not always supported this hypothesis, this may be due in part to allowing the tutors to cover different content than the noninteractive instruction. In 7 experiments, we tested the(More)
Why/AutoTutor is a tutoring system that helps students construct answers to qualitative physics problems by holding a conversation in natural language. Why/AutoTutor provides feedback to the student on what the student types in (positive, neutral, negative feedback), pumps the student for more information, prompts the student to fill in missing words, gives(More)
The interfaces of knowledge management systems will benefit from conversational agents, particularly for users who infrequently use such systems. The design of such agents will presumably share some of the dialog management facilities for systems designed for tutoring. For example, Why/AutoTutor is an automated physics tutor that engages students in(More)