Abstract
We evaluate whether semester-level interaction features from a large language model tutor improve assignment-score prediction beyond constant baselines. StudyChat provides 16,851 student-assistant exchanges from two offerings of an undergraduate artificial intelligence course, with outcomes matched to 175 of 203 student-semester records. Six fitted estimators and training-fold mean and median baselines share repeated nested cross-validation. After averaging each student's held-out predictions across ten repeats, Gradient Boosting achieves mean absolute error of 3.023 grade points, versus 3.138 for the median baseline. The paired difference is -0.114, with a conditional 95% bootstrap interval of [-0.245,0.013]. Representation changes and a semester-specific median yield similarly small gains. In the lowest observed-score quartile, however, boosting's error is 7.30 points, versus 8.07 for the median and 6.67 for the mean; Ridge has the lowest fitted-model error at 6.37 despite its worse overall error of 3.331. Scores concentrate near the ceiling: 160 students (91.4%) score above 90, and the lowest quartile contributes 62.1% of boosting's total absolute error. This retrospective comparison shows how baseline choice and error distribution affect predictive value, informing evaluation of student-support tools without establishing a learning effect.
Abstract (translated)
URL
https://arxiv.org/abs/2610.33488
