REC-CBM: Rubric-Aware Error-Correction Concept Bottleneck Models for Trustworthy Open-Ended Grading
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 emerged as a promising approach by routing predictions through human-interpretable concepts, providing
The increasing sophistication of LLMs is pushing the demand for more trustworthy and interpretable AI systems, especially in sensitive domains like education where verification is crucial.
This development addresses a key limitation of current AI models by making their decision-making processes transparent, fostering trust and enabling wider adoption in critical applications.
AI systems can now potentially offer verifiable rationales for their assessments, reducing black-box risks and improving human oversight in automated grading.
- · Education sector
- · Students
- · AI ethicists
- · Developers of interpretable AI
- · Traditional manual grading labor
- · Black-box AI models in sensitive workflows
Automated grading systems become more widely accepted and deployed in educational institutions.
Increased efficiency in grading allows educators to focus more on personalized instruction and curriculum development.
The principle of interpretable AI could extend to other critical domains, such as medical diagnostics and legal assessments, raising overall trust in AI applications.
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Read at arXiv cs.AI