From Feature-Based Models to Generative AI: Validity Evidence for Constructed Response Scoring

TL;DR

This paper compares feature-based and generative AI scoring methods for constructed responses, emphasizing the need for extensive validity evidence for generative AI due to transparency and consistency concerns.

Contribution

It highlights the differences in validity evidence requirements between feature-based and generative AI scoring systems and proposes best practices for validity evidence collection.

Findings

Generative AI scoring requires more validity evidence than feature-based methods.

Analysis of student essays demonstrates complexities in validating AI scoring systems.

Generative AI's lack of transparency impacts validity evidence collection.

Abstract

The rapid advancements in large language models and generative artificial intelligence (AI) capabilities are making their broad application in the high-stakes testing context more likely. Use of generative AI in the scoring of constructed responses is particularly appealing because it reduces the effort required for handcrafting features in traditional AI scoring and might even outperform those methods. The purpose of this paper is to highlight the differences in the feature-based and generative AI applications in constructed response scoring systems and propose a set of best practices for the collection of validity evidence to support the use and interpretation of constructed response scores from scoring systems using generative AI. We compare the validity evidence needed in scoring systems using human ratings, feature-based natural language processing AI scoring engines, and generative AI. The evidence needed in the generative AI context is more extensive than in the feature-based scoring context because of the lack of transparency and other concerns unique to generative AI such as consistency. Constructed response score data from a large corpus of independent argumentative essays written by 6-12th grade students demonstrate the collection of validity evidence for different types of scoring systems and highlight the numerous complexities and considerations when making a validity argument for these scores.