The study evaluates GPT‑5.5’s ability to grade handwritten physics assessments, using 10,364 scanned pages from 520 submissions by 416 candidates across a national Olympiad theory exam, a final selection camp, and a university quantum‑mechanics exam. Each submission was graded twice, with the second round incorporating refined instructions after analyzing first‑round disagreements. The AI’s total‑score correlations with official marks ranged from 0.91 to 0.97, and it successfully identified the same five‑student team for the final Olympiad selection as human graders, though exact partial‑credit grading—especially in experimental work—remained challenging. "Reliable AI grading therefore depends on detailed rubrics and should be used as a second reader or audit tool under examiner control."
arXiv:2608. 07523v1 Announce Type: cross Abstract: Difficulty differences across parallel-class programming examinations affect the fairness of course assessment.
arXiv:2609.14284v1 Announce Type: new Abstract: Criterion-level grading connects examination performance to learning outcomes, but manual marking introduces workload and variation between markers. Th...
Small language models can grade open‑ended exam answers as reliably as much larger models when they use an explicit rubric. In experiments with six cost‑efficient model configurations, the rubric decouples grading from judge intelligence, with answer identity explaining 95.6% of score variance and judge identity only 0.2%. Removing rubric criteria or the official answer collapses reliability and inflates scores, showing the rubric’s essential role.
arXiv:2606. 11477v1 Announce Type: cross Abstract: Correcting handwritten exams by hand is time-consuming and error-prone, particularly for large cohorts, while fully digital exams tend to force a didactic narrowing towards closed question formats.
arXiv:2608.22346v1 Announce Type: cross Abstract: This data article describes a multimodal collection of scanned examination answers paired with expert-designed Outcome-Based Education (OBE) grading...
Benchmark saturation and data contamination increasingly obscure genuine scientific reasoning in frontier LLMs. We introduce \textsc{ScienceArena}, an olympiad-style benchmark from thirteen public sci...
arXiv:2608.30517v1 Announce Type: new Abstract: Benchmark saturation and data contamination increasingly obscure genuine scientific reasoning in frontier LLMs. We introduce \textsc{ScienceArena}, an...
arXiv:2606. 24973v1 Announce Type: cross Abstract: We introduce a dataset of 32,534 double-marked real student responses to GCSE mock exams (GCSEs are the UK's national exams, taken at age ~16), spanning 328 questions across five subjects and including handwritten work.
arXiv:2608.21601v1 Announce Type: new Abstract: Benchmarks for scientific artificial intelligence are mostly written to be scored: multiple-choice questions, curated agent tasks with reference soluti...
The paper introduces THPT‑Ladder, a benchmark based on Vietnam’s 2025 National High School Graduation Examination’s convex grading scheme, which rewards partial credit non‑additively. It shows that standard accuracy metrics inflate model scores because they treat partial knowledge proportionally, whereas the official rubric penalizes incomplete correct sets. Using the benchmark, the authors demonstrate that this discrepancy can shift a model’s percentile ranking by up to 13 points among over 480,000 candidates.
arXiv:2607. 01247v1 Announce Type: cross Abstract: Open-ended mathematics exams are valuable because they assess reasoning, proof construction, algorithmic thinking, and communication of intermediate steps.
arXiv:2606. 12422v1 Announce Type: cross Abstract: The integration of large language models (LLMs) into educational assessment represents a transformative shift in classroom grading practices.