Example Concept: AI-driven assessments use algorithms to evaluate student work, providing quick feedback and identifying learning gaps. However, they must be calibrated to align with human grading standards, ensuring that AI assessments are fair and transparent. This involves training AI models on diverse datasets to avoid bias and regularly reviewing AI outputs to ensure consistency with educational goals.