AINeutralarXiv – CS AI · 9h ago6/10
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From Scoring to Explanations: Evaluating SHAP and LLM Rationales for Rubric-based Teaching Quality Assessment
Researchers propose a framework combining SHAP explainability with LLM-generated rationales to improve transparency in automated rubric-based scoring systems for educational assessment. Testing on classroom transcripts reveals fine-tuned language models outperform LLMs in accuracy, but SHAP attributions provide more faithful and transferable explanations than LLM rationales across different model architectures.