Example Concept: AI-driven assessment tools can adapt questions and grading criteria to match diverse learning needs by analyzing student performance data. However, ensuring that these tools do not inadvertently introduce bias or disadvantage certain student groups requires careful design and continuous monitoring. This involves training AI models on diverse datasets and regularly reviewing assessment outcomes to ensure equitable treatment of all students.