The Role of Implicit and Explicit Demographic Signals in Large Language Model-based Student Assessment

Research from arXiv explores how implicit and explicit demographic signals influence the performance of Large Language Models (LLMs) when used for student assessment. The study investigates the potential for both necessary demographic considerations, such as improving readability, and discriminatory outcomes, like lower scores for specific socioeconomic backgrounds. Controlled prompts were used to test these effects across Automated Essay Scoring, Formative Feedback, and Metalinguistic Question Answering tasks.

The widespread adoption of Large Language Models in sensitive domains like student assessment necessitates a thorough understanding of their potential biases and fairness implications. Ensuring equitable and accurate assessment outcomes is critical for maintaining trust in educational technologies and preventing systemic discrimination, which could have long-term societal impacts.

Large Language Models are increasingly common in student assessment.

Forward consideration, not a verified fact.