AI in Education · AI in Testing and Grading

Can AI Grading Introduce Bias Against Certain Writing Styles?

Yes — AI grading systems can inadvertently favor writing styles, structures, and vocabulary patterns most similar to their training data, which risks disadvantaging students who write in less conventional, culturally distinct, or non-native English patterns, making bias a genuine and actively studied concern in automated essay scoring.

Key takeaways

AI grading systems learn patterns from training data, which means writing styles well represented in that data tend to score more favorably than unconventional or underrepresented styles.
Non-native English speakers and students from different cultural or dialectal backgrounds are among the groups most frequently cited as at risk of unfair scoring.
Unconventional but genuinely strong writing — creative structure, distinctive voice — can be undervalued by systems trained to reward more conventional patterns.
Testing organizations and researchers have actively studied and worked to identify and reduce these biases, though it remains an acknowledged, ongoing challenge.

A Real and Studied Risk, Not a Hypothetical One

AI grading systems learn to score writing by identifying statistical patterns in large sets of previously scored essays — patterns that correlate with high or low scores in that training data. This creates a genuine, well-recognized risk: writing styles, structures, and vocabulary choices that are well represented among the training data’s higher-scoring examples tend to be implicitly rewarded, while equally strong writing that takes a less conventional or underrepresented approach can be unfairly undervalued, simply because it doesn’t match the statistical patterns the system learned to associate with quality.

This isn’t a hypothetical or fringe concern — it’s an active area of research and scrutiny within the assessment field, precisely because the stakes of unfair scoring in educational and testing contexts are significant, potentially affecting grades, academic standing, or even admissions and scholarship decisions.

Who Faces the Greatest Risk

Certain groups of students are more frequently cited in discussions of this bias risk. Non-native English speakers often write with structures, phrasing, or vocabulary choices shaped by their first language, which can differ systematically from patterns typical among native English writers well represented in much training data — a mismatch that risks scoring genuinely strong writing lower simply because it doesn’t fit the statistical mold the system learned. Students from different cultural or dialectal backgrounds can face a similar risk if their natural voice or rhetorical style diverges from the dominant patterns in a scoring system’s training data.

There’s also a more general concern about creative or unconventional writers — students who intentionally break from a standard five-paragraph essay structure, use distinctive stylistic choices, or take creative risks, potentially producing genuinely compelling writing that an automated system trained to reward more conventional patterns might penalize rather than reward.

What’s Being Done, and What Still Remains a Challenge

Testing organizations and researchers working on automated essay scoring have invested real effort into identifying and reducing these bias risks — testing systems against diverse groups of writers, analyzing score disparities across demographic groups, and refining models to reduce unfair patterns where they’re found. This is genuine, ongoing work, and progress has been made in understanding and addressing specific known bias patterns. That said, bias in AI grading remains an acknowledged, active challenge rather than a fully solved problem, which is a core reason many responsible deployments of AI essay grading maintain human review, particularly for high-stakes assessments where the consequences of an unfair score are significant.

Bottom Line

AI grading systems can genuinely introduce bias against certain writing styles, particularly affecting non-native English speakers, students from different cultural or dialectal backgrounds, and unconventional writers, because these systems learn from training data that may not represent the full diversity of legitimate writing styles equally well — a real, actively studied risk that’s part of why human oversight remains important in high-stakes AI-assisted grading.

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Important caveats

The degree of bias risk varies by specific tool and how carefully it was developed and tested, so it shouldn't be assumed that all AI grading systems carry an equal level of risk.

Frequently asked questions

Why would an AI grading system favor certain writing styles over others?

AI scoring systems are typically trained on large sets of previously scored essays, learning statistical patterns associated with higher and lower scores, which means writing styles, structures, and vocabulary well represented among the higher-scoring training examples tend to be favored, potentially at the expense of equally strong but differently styled writing.

Are non-native English speakers particularly at risk from AI grading bias?

This is one of the most frequently cited concerns in research and commentary on automated essay scoring, since non-native English writing patterns can differ systematically from the patterns most represented in typical training data, potentially leading to unfairly lower scores despite genuinely strong content or reasoning.

What are testing organizations doing to address this bias risk?

Many organizations that develop or use automated scoring systems have invested in bias research, testing systems across diverse groups of writers, and refining models to reduce disparities, though this remains an active, ongoing area of work rather than a fully solved problem.

Sources

[1]Educational Testing Service — ETS
[2]Education Research and Reports — Brookings Institution

Written by Editorial Team

Last updated July 28, 2026

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