8 articles tagged with #automated-grading. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.
Researchers tested large language models against human examiners on 32,534 real UK GCSE exam responses, finding that top-performing models achieve higher agreement with examiner consensus than examiners do with each other. The results demonstrate LLMs can reliably grade subjective tasks like essays and handle complex handwritten work, suggesting viable automated marking solutions.
Researchers introduce HG-Bench, a benchmark dataset of 500 annotated homework samples for evaluating automated grading systems' ability to locate and decompose handwritten student answers across multiple pages. Current AI models, including frontier VLMs, achieve less than 55% accuracy on complete answer localization, revealing a significant capability gap in understanding spatial reasoning structures in handwritten documents.
Researchers demonstrate that vision-language foundation models can achieve 98.4% accuracy in automatically grading handwritten exam answers, compared to previous methods' 88-91%. The approach prioritizes fairness by minimizing false negatives that disadvantage students and shows promise for scalable, automated exam grading without sacrificing pedagogical quality.
Researchers evaluated how large language models (GPT and Grok) perform at grading graduate-level research reports, finding significant inconsistencies both within individual models and between different models. The study reveals that interaction history causes models to systematically drift from human grading standards, raising concerns about fairness in automated academic assessment.
Researchers propose REC-CBM, a novel machine learning model that combines concept bottleneck models with rubric-aware error correction to automate open-ended educational grading while maintaining transparency and interpretability. Unlike black-box LLM systems, REC-CBM allows educators to verify scoring decisions through human-interpretable concept reasoning, addressing the growing need for trustworthy automated grading in educational settings.
Researchers developed a human-in-the-loop LLM system for grading handwritten mathematics assessments that reduces grading time by 23% while maintaining accuracy comparable to manual grading. The system combines automated scanning, multi-pass LLM scoring, consistency checks, and mandatory human verification to handle pen-and-paper tests at scale.
Researchers introduce CARO (Confusion-Aware Rubric Optimization), a new framework that improves LLM-based automated grading by using confusion matrices to separate and fix specific error patterns instead of aggregating all errors together. This approach prevents conflicting constraints and significantly outperforms existing methods in teacher education and STEM datasets.
Researchers introduce GUIDE, a new framework for improving automated grading of student responses using large language models. The system addresses key limitations in current LLM-based grading by optimizing the selection of training examples and generating better explanations for scoring decisions.
