This work is like a road test and safety inspection for AI tools that grade or review student essays. It checks how accurate, fast, and fair they are compared with human graders.
Universities and schools are considering AI tools to help mark essays or provide feedback, but they worry about accuracy, grading speed, and potential bias against certain student groups. The study systematically evaluates these dimensions so institutions can make evidence-based decisions about adopting AI for assessment.
Evidence base and methodology for auditing AI in assessment contexts; alignment with academic assessment standards and bias/fairness frameworks rather than just generic model performance.
Unknown
Unknown
Medium (Integration logic)
Context-specific validity: performance and bias characteristics may not transfer cleanly across subjects, languages, and student populations, requiring repeated local validation.
Early Majority
Focuses on empirical evaluation of accuracy, efficiency, and bias in AI-based essay assessment rather than just showcasing capabilities, positioning it as a decision-support resource for institutional adoption rather than a product pitch.