Making computer tutors more like humans
Journal of Artificial Intelligence in Education
Using Grice's maxim of quantity to select the content of plan descriptions
Artificial Intelligence
Providing adaptive support to the understanding of instructional material
Proceedings of the 6th international conference on Intelligent user interfaces
A Model for Adapting Explanations to the User‘s Likely Inferences
User Modeling and User-Adapted Interaction
Example Explanation in Learning Environments
ITS '96 Proceedings of the Third International Conference on Intelligent Tutoring Systems
Plan-based dialogue management in a physics tutor
ANLC '00 Proceedings of the sixth conference on Applied natural language processing
Using Bayesian Networks to Manage Uncertainty in Student Modeling
User Modeling and User-Adapted Interaction
Teachers' and tutors' social reflection around SenseCam images
International Journal of Human-Computer Studies
Intelligent tutoring systems: new challenges and directions
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
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We describe a framework that helps students learn from examples by generating example problem solutions whose level of detail is tailored to the students' domain knowledge. The framework uses natural language generation techniques and a probabilistic student model to selectively introduce gaps in the example solution, so that the student can practice applying rules learned from previous examples in problem solving episodes of difficulty adequate to her knowledge. Filling in solution gaps is part of the meta-cognitive skill known as self-explanation (generate explanations to oneself to clarify an example solution), which is crucial to effectively learn from examples. In this paper, we describe how examples with tailored solution gaps are generated and how they are used to support students in learning through gap-filling self-explanation.