AI RESEARCH
Marked Pedagogies: Examining Linguistic Biases in Personalized Automated Writing Feedback
arXiv CS.CL
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ArXi:2603.12471v1 Announce Type: new Effective personalized feedback is critical to students' literacy development. Though LLM-powered tools now promise to automate such feedback at scale, LLMs are not language-neutral: they privilege standard academic English and reproduce social stereotypes, raising concerns about how "personalization" shapes the feedback students receive. We examine how four widely used LLMs (GPT-4o, GPT-3.5-turbo, Llama-3.3 70B, Llama-3.1 8B) adapt written feedback in response to student attributes.