AI RESEARCH
LLM Essay Scoring Under Holistic and Analytic Rubrics: Prompt Effects and Bias
arXiv CS.AI
•
ArXi:2604.00259v1 Announce Type: cross Despite growing interest in using Large Language Models (LLMs) for educational assessment, it remains unclear how closely they align with human scoring. We present a systematic evaluation of instruction-tuned LLMs across three open essay-scoring datasets (ASAP 2.0, ELLIPSE, and DREsS) that cover both holistic and analytic scoring. We analyze agreement with human consensus scores, directional bias, and the stability of bias estimates.