<![CDATA[Automated essay scoring in education is increasingly expected to do more than reproduce human holistic scores; classroom use also demands rubric-aligned feedback, transparent evidence, and a way to route uncertain cases to teachers. In this study, “LLM-ready” refers to a system that outputs structured score evidence, weak-trait signals, and document-level anchors that can later be verbalized by a language model without changing the underlying decision trace. This study aimed to evaluate whether a rubric-grounded, LLM-ready pipeline can achieve competitive scoring accuracy while also generating auditable formative feedback and a teacher-controllable review signal. The evaluation used the public ASAP corpus of 12,976 essays across eight prompts and prompt-wise five-fold cross-validation. Four holistic scorers were compared: length-only, rubric forest, prompt-adaptive centroid regressor (PACR), and the final RG-Score ensemble with trait grounding, isotonic calibration, and audit control. Auxiliary analytic scoring was examined on Prompts 2 and 7–8, and feedback experiments were conducted on all 2,292 essays from Prompts 7 and 8. PACR obtained the highest macro QWK of 0.739, while RG-Score reached 0.738 and provided a calibrated, auditable path to feedback. The prompt-level QWK for RG-Score ranged from 0.66 to 0.82, with particularly strong gains on Prompts 6 and 7. Auxiliary analytic scoring yielded QWK values of 0.623 for Prompt 2 domain2, 0.604 on average for Prompt 7 traits, and 0.506 on average for Prompt 8 traits. The rubric-grounded evidence feedback template achieved a Trait Recall@2 of 0.829, a valid evidence rate of 0.912, and an auditability index of 0.893 on Prompts 7 and 8. These findings support rubric-grounded AES as a practical assessment-support approach for secondary-school writing and as a structured foundation for higher-education formative feedback workflows, while also indicating that weaker trait models should be treated as advisory rather than fully autonomous.]]>
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