PsyScore: A Psychometrically-Aware Framework for Trait-Adaptive Essay Scoring and ZPD-Scaffolded Feedback
arXiv:2606.20287v1 Announce Type: new 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 PsyScore, a psychometrically-aware framework that integrates diagnostic assessment with instructional scaffolding through a sha
The proliferation of advanced neural networks and large language models creates an immediate need for more sophisticated and diagnostically useful automated assessment tools in education.
Improving automated essay scoring and feedback addresses critical limitations in AI-driven educational systems, moving towards more personalized and effective learning pathways.
Traditional, less nuanced automated essay grading is challenged by systems like PsyScore, which offer integrated diagnostic assessment and proficiency-aware feedback.
- · Educational technology providers
- · Students
- · Teachers
- · AI developers in education
- · Traditional automated essay scoring platforms
- · Educators relying solely on manual grading for large cohorts
More accurate and personalized essay scoring and feedback become widely available, enhancing learning outcomes.
Educational institutions adopt AI-driven tools for more effective and scalable assessment, potentially leading to curriculum adjustments.
The role of human educators evolves to focus more on higher-order teaching and less on rote assessment, as AI handles diagnostic feedback.
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