Fairness and Effectiveness in AI-Driven Educational Assessments: Challenges and Mitigation Strategies

Abstract

Artificial Intelligence (AI) is increasingly integrated into educational assessment, with the potential benefits of greater objectivity, efficiency, and personalized evaluation. The drawbacks are the deployment of AI-based tools, for example in the forms of automatic scoring and adaptive testing. Such tools greatly exacerbate issues of equity and fairness, particularly with respect to algorithmic bias. This article provides an overview of the capabilities and pitfalls of using AI for student assessment. It highlights, via a generic case, the ability of bias to either maintain or intensify existing disparities in education through inappropriate training data or the misspecification of algorithms. Events such as those that transpired during the 2020 UK A-levels, or the problems with plagiarism software, could only be a few examples of the associated risks. Proper risk mitigation requires diversity and representative datasets, algorithmic transparency, strong human oversight, and formal accountability mechanisms within institutions. Certification standards for assessment tools and periodic bias audits are also crucial. In the end, building AI fluency amid teachers and learners is key for making sure AI test tools are made and used right, pushing equality and helping all of education fairly.