Research on AI Marking and Assessment in Schools

Understanding the evidence behind AI-assisted assessment, learning analytics and teacher-moderated marking systems.

Why Assessment Research Matters

High-quality feedback is one of the most powerful influences on student learning. Decades of research demonstrate that clear, timely feedback helps students identify misconceptions, refine their understanding, and improve future performance.

Advances in artificial intelligence and learning analytics now offer new opportunities to support assessment processes. When carefully designed and combined with teacher oversight, AI systems can help analyse large volumes of student responses, highlight patterns in performance, and provide structured feedback aligned to assessment criteria.

What the Research Shows

Feedback and Learning

Research consistently shows that feedback is one of the most influential factors affecting student progress. Structured feedback that clearly explains strengths, misconceptions, and next steps can significantly improve student understanding.

Teacher Workload

Providing high-quality feedback requires time. Reducing repetitive marking workload allows teachers to focus on interpreting results, supporting students, and planning targeted instruction.

Learning Analytics

Assessment data becomes most valuable when it reveals common misconceptions, topic-level weaknesses, and question-level performance trends.

Evidence-Informed Design

Academic References

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