Bias Taxonomy and Mitigation Pipelines for AI-Based Educational Assessment Systems

Algorithmic assessment systems are increasingly evaluating and scoring essays while generating and providing feedback. These systems also are used to monitor remote examinations and providing assistance in classifying academic risk. These systems offer attractive promises of rapid processing, consistency and scalability. However, the benefits of these systems come with the technical risk is that model behavior may vary across student groups in ways that are difficult to detect and can impact the accuracy and effectiveness of the systems. This paper develops a technically oriented version of the Equitable AI Assessment Framework focused on bias classification, fairness measurement, and mitigation pipelines for AI-based educational assessment systems. The framework distinguishes data-related, algorithmic, and interaction-driven sources of bias; maps common fairness criteria to educational use cases; and organizes mitigation strategies into pre-processing, in-processing, and post-processing stages. The contribution is not as much as a broad institutional governance model, and rather more of a practical fairness engineering structure that can guide developers, vendors, and technically informed evaluators as they design, test, and monitor assessment tools. The paper considers treating fairness as an measurable system property rather than a general ethical aspiration and provides a more operational approach to reducing unequal error, improving subgroup validity, and documenting technical tradeoffs in automated assessment environments.