An Evaluation of a Hybrid Language Understanding Approach for Robust Selection of Tutoring Goals
International Journal of Artificial Intelligence in Education
Taking Control of Redundancy in Scripted Tutorial Dialogue
Proceedings of the 2005 conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology
Analyzing Completeness and Correctness of Utterances Using an ATMS
Proceedings of the 2005 conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology
Combining Bayesian Networks and Formal Reasoning for Semantic Classification of Student Utterances
Proceedings of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work
Understanding complex natural language explanations in tutorial applications
ScaNaLU '06 Proceedings of the Third Workshop on Scalable Natural Language Understanding
Recognizing entailment in intelligent tutoring systems*
Natural Language Engineering
Interpretation and generation in a knowledge-based tutorial system
KRAQ '06 Proceedings of the Workshop KRAQ'06 on Knowledge and Reasoning for Language Processing
Evidence of Misunderstandings in Tutorial Dialogue and their Impact on Learning
Proceedings of the 2009 conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling
Natural language dialog with a tutor system for mathematical proofs
Proceedings of the 2005 joint Chinese-German conference on Cognitive systems
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The Why2-Atlas tutoring system presents students with qualitative physics questions and encourages them to explain their answers through natural language. Although there are inexpensive techniques for analyzing explanations, we claim that better understanding is necessary for use within tutoring systems. In this paper we motivate and describe how the system creates and uses a deeper proof-based representation of student essays in order to provide students with substantive feedback on their explanations. We describe in detail the abductive reasoner, Tacitus-lite+, that we use within the tutoring system. We also discuss evaluation results for an early version of the Why2-Atlas system and a subsequent evaluation of the theorem-proving module. We conclude with the discussion of work in progress and additional future work for deriving more benefits from a proof-based approach for tutoring applications.