Evaluating a Data-Driven Redesign Process for Intelligent Tutoring Systems
Abstract
Past research has defined a general process for the data-driven redesign of educational technologies and has shown that in carefully-selected instances, this process can help make systems more effective. In the current work, we test the generality of the approach by applying it to four units of a middle-school mathematics intelligent tutoring system that were selected not based on suitability for redesign, as in previous work, but on topic. We tested whether the redesigned system was more effective than the original in a classroom study with 123 students. Although the learning gains did not differ between the conditions, students who used the Redesigned Tutor had more productive time-on-task, a larger number of skills practiced, and greater total knowledge mastery. The findings highlight the promise of data-driven redesign even when applied to instructional units *not* selected as likely to yield improvement, as evidence of the generality and wide applicability of the method.
Cite
@article{arxiv.2603.29094,
title = {Evaluating a Data-Driven Redesign Process for Intelligent Tutoring Systems},
author = {Qianru Lyu and Conrad Borchers and Meng Xia and Karen Xiao and Paulo F. Carvalho and Kenneth R. Koedinger and Vincent Aleven},
journal= {arXiv preprint arXiv:2603.29094},
year = {2026}
}
Comments
Accepted as short paper to the 27th International Conference on Artificial Intelligence in Education (AIED 2026)