#automated-grading News & Analysis

4 articles tagged with #automated-grading. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

4 articles

AINeutralarXiv – CS AI · 5d ago6/10

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REC-CBM: Rubric-Aware Error-Correction Concept Bottleneck Models for Trustworthy Open-Ended Grading

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.

AIBullisharXiv – CS AI · Mar 166/10

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Human-in-the-Loop LLM Grading for Handwritten Mathematics Assessments

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.

AINeutralarXiv – CS AI · Mar 34/106

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Confusion-Aware Rubric Optimization for LLM-based Automated Grading

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.

AINeutralarXiv – CS AI · Mar 34/106

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Optimizing In-Context Demonstrations for LLM-based Automated Grading

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.