Using large language models to evaluate ethical persuasion text: A measurement modeling approach

Abstract

As AI becomes prevalent in all stages of assessment procedures, it is essential to develop procedures to ensure that its use supports ethical and psychometrically defensible measurement. In this study, we consider how measurement principles can be directly incorporated into an ethical reasoning performance assessment in which Large Language Models (LLMs) serve as raters. We demonstrate how a measurement approach can be used to obtain defensible measures of LLM-generated text related to ethics, prompts designed to elicit text-based ethical persuasion responses, and individual learners. We demonstrate how measurement quality indicators can serve as guardrails to help mitigate potential AI-related risks that can impact learners, such as hallucinations or errors. We describe a novel approach to designing, implementing, and evaluating performance assessments with AI, with the goal of enabling effective personalized learning experiences.

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