AI RESEARCH
Interpretability from the Ground Up: Stakeholder-Centric Design of Automated Scoring in Educational Assessments
arXiv CS.CL
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ArXi:2511.17069v3 Announce Type: replace AI-driven automated scoring systems offer scalable and efficient means of evaluating complex student-generated responses. Yet, despite increasing demand for transparency and interpretability, the field has yet to develop a widely accepted solution for interpretable automated scoring to be used in large-scale real-world assessments. This work takes a principled approach to address this challenge.