GradeDrift-LLM: Measuring Student-History-Induced Score Drift in LLM-Based Automated Grading

Abstract

Background: Large language models (LLMs) are increasingly explored for automated educational assessment, while future educational platforms may combine grading, feedback, learner analytics, and personalization. The objective of this study was to determine whether student-history metadata can influence the numerical score assigned to the same answer. Methods: This study introduces GradeDrift-LLM, a controlled framework for measuring student-history-induced score drift in LLM-based automated grading. We evaluated 1000 Computer Science answers from 100 students across six student-history conditions and eight open-weight LLMs. For each grading instance, the submitted answer, question, reference answer, rubric-related information, scoring scale, and grading instruction were kept constant; only the student-history condition varied. Results: Across 39,997 valid paired comparisons, 83.92% showed no drift, 9.40% showed upward drift, and 6.68% showed downward drift. Mean absolute drift was 0.2137 points, and the 95th percentile absolute drift was 1 point. Positive-history frames tended to increase scores, whereas negative-history frames tended to decrease them. Drift was model-dependent, not uniformly explained by approximate scale, and present in both technical and argumentative answers; rare extreme deviations reached 10 points. Conclusions: Student-history metadata can influence LLM-generated grading scores despite explicit instructions to ignore it. Future LLM-based grading systems should separate answer-based scoring from learner-context-based personalization and validate score invariance under controlled learner-context variations.