We introduce an evidence-centered, argument-based framework for validating AI-generated assessment items. The framework organizes three claims—content comparability, construct validity, and psychometric functioning—with assumptions and evidence. Applying the framework to critical thinking assessments, we compared AI-generated and human-authored items and identified distinct mechanisms underlying the validity of AI-generated items.

Automated scoring of written and spoken responses is an NLP application that can significantly impact lives especially when deployed as part of high-stakes tests such as the GRE® and the TOEFL®. Ethical considerations require that automated scoring algorithms treat all test-takers fairly. The educational measurement community has done significant research on fairness in assessments and automated scoring systems must incorporate their recommendations. The best way to do that is by making available automated, non-proprietary tools to NLP researchers that directly incorporate these recommendations and generate the analyses needed to help identify and resolve biases in their scoring systems. In this paper, we attempt to provide such a solution.