Ari Bader-Natal

New Publication: "Motivating Appropriate Challenges in a Reciprocal Tutoring System"

Bader-Natal, A. and Pollack, J.B. Motivating Appropriate Challenges in a Reciprocal Tutoring System. Proceedings of the 12th International Conference on Artificial Intelligence in Education, IOS Press, 2005. Nominated for Best Paper Award.
Abstract Draft PDF ACM Digital Library

Proceedings of the 12th International Conference on Artificial Intelligence in Education, IOS Press, 2005.

Formalizing a student model for an educational system requires an engineering effort that is highly domain-specific. This model-specificity limits the ability to scale a tutoring system across content domains. In this work we offer an alternative, in which the task of student modeling is not performed by the system designers. We achieve this by using a reciprocal tutoring system in which peer-tutors are implicitly tasked with student modeling. Students are motivated, using the Teacher's Dilemma, to use these models to provide appropriately-difficult challenges. We implement this as a basic literacy game in a spelling-bee format, in which players choose words for each other to spell across the internet. We find that students are responsive to the game's motivational structure, and we examine the affect on participants' spelling accuracy, challenge difficulty, and tutoring skill.