A new study found that artificial intelligence (AI) gave more praise and positive feedback to Black students’ essays and differing treatment for other students based on their race and sex.
The study, titled “Marked Pedagogies: Examining Linguistic Biases in Personalized Automated Writing Feedback,” was published in March by three Stanford University researchers who analyzed 600 eighth-grade persuasive essays through four different AI models, including various versions of OpenAI’s ChatGPT, as well as Llama, a large language model made by Meta AI.
The essays covered topics including whether schools should require community service and whether aliens built a hill on Mars.
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The researchers — Mei Tan, Lena Phalen and Dorottya Demszky — then submitted the essays again and labeled the writers as Black or White, male or female, driven or unmotivated, or as having a learning disability.
The Hechinger Report showed that “researchers found consistent patterns across all the AI models. Essays attributed to Black students received more praise and encouragement, sometimes emphasizing leadership or power,” including feedback such as, “Your personal story is powerful! Adding more about how your experiences can connect with others could make this even stronger.”
Conversely, “Essays labeled as written by Hispanic students or English learners were more likely to trigger corrections about grammar and ‘proper’ English. When the student was identified as White, the feedback more often focused on argument structure, evidence and clarity — the kinds of comments that can push writers to strengthen their ideas.”
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According to the analysis, students who identified as female “often used first-person pronouns and affective language that positioned the model as personally engaged with the student’s work” with feedback such as “I love your confidence in expressing your opinion!” and “I appreciate your emphasis on respect.”
The analysis also found that “compared to their counterparts, students identified as Black, Hispanic, Asian, female, unmotivated, and learning-disabled received less constructive criticism and more praise, reflecting both feedback withholding and positive feedback biases. In some cases, praise took on overtly stereotyped forms: words like ‘love’ were used disproportionately with female students, while ‘powerful’ appeared only for Black students.”
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Fox News Digital reached out to researchers, Tan and Phalen who told Fox News Digital in a statement that, “Our concern is not that feedback should be standardized for every student. Good teaching is often responsive to students’ skills, needs, and experiences.”
They continued, “Feedback being positive does not mean it’s high-quality. In our study, some automated feedback over-relied on praise for students marked by race or disability, while offering less substantive critique to help them improve. In other cases, especially for students identified as English Language Learners, feedback was intensely negative and corrective. Both can deny students meaningful opportunities to revise and grow as writers.”
“Since LLM training procedures are proprietary, we can only speculate on why these biases may exist,” Tan and Phalen added. “Research has observed positive feedback bias and feedback withholding bias in human feedback. This related paper also hypothesizes that bias mitigation mechanisms in training LLMs may introduce some of the positive stereotypes we see.”
Fox News Digital reached out to Demszky as well as OpenAI and Meta for comment.
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