Bias in AI Grading

Automated essay scoring systems can estimate a score from word choice, grammar, organization, argument structure, prompt relevance, or patterns learned from scored examples. A model may align with human ratings on an overall sample while behaving differently for particular writing styles or student groups. Some students use dialects, multilingual structures, assistive technology, or alternative communication patterns that an evaluation set may underrepresent. Do not treat every score difference as proof of discrimination, and do not treat a high agreement average as proof of fairness. Check the rubric, prompt, sample composition, score distribution, and error types. Review whether the tool rewards length, vocabulary, spelling, or formulaic structures more than the learning objective requires. Compare model scores with trained human ratings and inspect cases where raters disagree. For important decisions, keep a qualified teacher in the review loop and provide a process for students to ask questions or correct a record. Recent research has examined how the demographic composition of training data affects fairness in fine-tuned LLM essay scoring on a particular essay corpus. Findings are specific to the models, data, and evaluation design studied; they do not establish how another classroom’s system will behave. Schools should test the actual tool, prompts, grade levels, languages, and writing assignments they use. Report sample sizes, uncertainty, and limitations. If a model’s feedback discourages a student or misreads the content, correct the assessment and examine the cause before expanding use.