Interpretability from the Ground Up: Stakeholder-Centric Design of Automated Scoring in Educational Assessments

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.We analyze the needs and potential benefits of interpretable automated scoring for various assessment stakeholder groups and develop four principles of interpretability -Faithfulness, Groundedness, Traceability, and Interchangeability (FGTI)targeted at those needs.To illustrate the feasibility of implementing these principles, we develop the ANALYTICSCORE framework 1 as a reference framework.When applied to the domain of text-based constructed-response scoring, ANALYTICSCORE outperforms many uninterpretable scoring methods in terms of scoring accuracy and is, on average, within 0.06 QWK of the uninterpretable SOTA across 10 items from the ASAP-SAS dataset.By comparing against human annotators conducting the same featurization task, we further demonstrate that the featurization behavior of ANALYTICSCORE aligns well with that of humans.