REC-CBM: Rubric-Aware Error-Correction Concept Bottleneck Models for Trustworthy Open-Ended Grading
📰 ArXiv cs.AI
Learn to build trustworthy open-ended grading systems using rubric-aware error-correction concept bottleneck models (REC-CBM) for equitable and personalized education
Action Steps
- Build a concept bottleneck model (CBM) using a large language model (LLM) as the base architecture
- Integrate rubric-aware error-correction mechanisms into the CBM to improve scoring accuracy and transparency
- Train the REC-CBM model on a dataset of graded open-ended responses to learn the patterns and relationships between the rubric and the responses
- Evaluate the performance of the REC-CBM model using metrics such as accuracy, precision, and recall
- Apply the REC-CBM model to automate open-ended grading tasks, providing educators with trustworthy and explainable scoring results
Who Needs to Know This
Educators and AI researchers can benefit from this technique to develop trustworthy automated grading systems, improving the efficiency and accuracy of open-ended assessments
Key Insight
💡 Rubric-aware error-correction concept bottleneck models can improve the accuracy and transparency of automated open-ended grading systems
Share This
📚💻 Introducing REC-CBM: a rubric-aware error-correction concept bottleneck model for trustworthy open-ended grading #AIinEducation #AutomatedGrading
Key Takeaways
Learn to build trustworthy open-ended grading systems using rubric-aware error-correction concept bottleneck models (REC-CBM) for equitable and personalized education
Full Article
Title: REC-CBM: Rubric-Aware Error-Correction Concept Bottleneck Models for Trustworthy Open-Ended Grading
Abstract:
arXiv:2605.27402v1 Announce Type: cross Abstract: Open-ended grading is central to equitable and personalized education, yet manual grading remains time-consuming and costly, underscoring the need for automated grading systems. Although recent neural and large language model (LLM) based systems have demonstrated superior performance, they are typically black-box models whose scoring processes and rationales are difficult for educators to verify and trust. Concept bottleneck models (CBMs) have em
Abstract:
arXiv:2605.27402v1 Announce Type: cross Abstract: Open-ended grading is central to equitable and personalized education, yet manual grading remains time-consuming and costly, underscoring the need for automated grading systems. Although recent neural and large language model (LLM) based systems have demonstrated superior performance, they are typically black-box models whose scoring processes and rationales are difficult for educators to verify and trust. Concept bottleneck models (CBMs) have em
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