Introduction
The rapid integration of artificial intelligence into global education has reached an inflection point. By 2025, adoption rates among university students are estimated to exceed 92%, fundamentally reshaping how learning, assessment, and academic integrity are managed.
Yet beneath this rapid uptake lies a critical threat: systemic bias. Rather than functioning as neutral tools, many AI systems reproduce and intensify the social and institutional inequities already embedded within education. Nowhere is this more visible than in AI-driven assessment and surveillance technologies.
This article examines how biased AI systems are reshaping academic evaluation, discipline, and student trust.
1. The Crisis of Biased Policing: AI Detection Tools
AI detection tools, introduced to protect academic integrity, have unintentionally created a new system of algorithmic surveillance that disproportionately impacts marginalized students.
Bias Against Non-Native English Speakers (NNES)
AI detectors consistently misclassify the writing of non-native English speakers as AI-generated. Research has shown that up to 61.22% of NNES essays are falsely flagged, with one widely used system incorrectly labeling 98% of TOEFL essays as machine-written. These systems equate linguistic simplicity and structural predictability with artificial generation, penalizing students for language patterns shaped by learning context rather than misconduct.
The Paradox of Evasion
This bias creates a damaging feedback loop. Students who fear false accusations may feel compelled to use generative AI to “polish” their language, ironically increasing dependence on the very tools they are accused of misusing. For international students, these false positives carry serious consequences, including academic penalties and loss of merit-based funding.
Targeting Dialectal Differences
Bias extends beyond second-language writing to dialect. Studies show that AI systems associate African American English (AAE) with lower-prestige attributes and harsher judgments. In simulated criminal-justice scenarios, AAE speakers faced conviction rates of 68.7%, compared to 62.1% for speakers of standardized American English. When such biases migrate into educational contexts, they risk reinforcing long-standing racial inequities under the guise of automation.
2. Algorithmic Discrimination in Classroom Decision-Making
AI tools used to assist teachers with classroom management introduce bias into everyday pedagogical decisions.
Punitive Behavior Recommendations
A 2025 study on AI teacher assistants found that platforms generated more punitive behavior intervention plans for hypothetical students with Black-coded names than for those with white-coded names. This is especially concerning given that Black students already experience disproportionate disciplinary actions for subjective behaviors such as “disruption” or “defiance.”
Biased Grading Systems
Similarly, automated grading systems have been shown to replicate racial bias, assigning consistently lower scores to Black students compared to Asian students. When embedded into assessment pipelines, such systems can quietly shape academic trajectories with long-term consequences.
Closing Reflection
When AI systems are used to monitor, evaluate, and discipline students, bias becomes operationalized on a large scale. Without transparency and human oversight, algorithmic tools risk transforming education from a space of growth into one of automated suspicion.
