Can Large Language Models Differentiate Harmful from Argumentative Essays? Steps Toward Ethical Essay Scoring
This addresses a critical gap in educational technology for students and educators by highlighting ethical risks in automated essay scoring, though it is incremental as it identifies issues without proposing new solutions.
This study tackled the problem of Automated Essay Scoring (AES) systems and Large Language Models (LLMs) failing to identify harmful content in essays, such as racism and gender bias, by introducing the Harmful Essay Detection (HED) benchmark. The results showed that LLMs require improvement to differentiate harmful from argumentative essays and that both AES models and LLMs neglect ethical dimensions in scoring.
This study addresses critical gaps in Automated Essay Scoring (AES) systems and Large Language Models (LLMs) with regard to their ability to effectively identify and score harmful essays. Despite advancements in AES technology, current models often overlook ethically and morally problematic elements within essays, erroneously assigning high scores to essays that may propagate harmful opinions. In this study, we introduce the Harmful Essay Detection (HED) benchmark, which includes essays integrating sensitive topics such as racism and gender bias, to test the efficacy of various LLMs in recognizing and scoring harmful content. Our findings reveal that: (1) LLMs require further enhancement to accurately distinguish between harmful and argumentative essays, and (2) both current AES models and LLMs fail to consider the ethical dimensions of content during scoring. The study underscores the need for developing more robust AES systems that are sensitive to the ethical implications of the content they are scoring.