@inproceedings{xia-etal-2026-psyscore,
    title = "{P}sy{S}core: A Psychometrically-Aware Framework for Trait-Adaptive Essay Scoring and {ZPD}-Scaffolded Feedback",
    author = "Xia, Wei  and
      Wu, Jin  and
      Shi, Haoran  and
      Wang, Xiangyu  and
      Zheng, Chanjin",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.384/",
    pages = "7768--7786",
    ISBN = "979-8-89176-395-1",
    abstract = "Effective Automated Essay Scoring (AES) are expected to support both reliable assessment and actionable instructional feedback. However, existing approaches often treat scoring and feedback as separate components: neural scoring models provide limited interpretability, while Large Language Model (LLM)-based feedback is typically insensitive to learners' proficiency levels. To address this fragmentation, this work proposes \textbf{PsyScore}, a psychometrically-aware framework that integrates diagnostic assessment with instructional scaffolding through a shared latent ability representation. PsyScore comprises three key modules: a \textbf{Trait-Adaptive Neural IRT Scorer} that incorporates the Graded Partial Credit Model (GPCM) into a neural architecture, enabling the precise estimation of student ability while maintaining psychometric interpretability, a \textbf{ZPD-Scaffolded Feedback Generator}, which conditions multi-agent feedback strategies on the diagnosed ability parameter to adapt instructional focus across different proficiency levels, and a \textbf{Multi-Perspective Feedback Evaluation Strategy} that assesses feedback quality via pairwise preference judgments and student revision simulations. Experiments on the ASAP++ dataset demonstrate that PsyScore achieves competitive scoring performance while providing more pedagogically aligned feedback."
}