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🧠 AI🟢 BullishImportance 6/10
Human-in-the-Loop LLM Grading for Handwritten Mathematics Assessments
arXiv – CS AI|Arne Vanhoyweghen, Vincent Holst, Melika Mobini, Lukas Van de Voorde, Tibo Vanleke, Bert Verbruggen, Brecht Verbeken, Andres Algaba, Sam Verboven, Marie-Anne Guerry, Filip Van Droogenbroeck, Vincent Ginis|
🤖AI Summary
Researchers developed a human-in-the-loop LLM system for grading handwritten mathematics assessments that reduces grading time by 23% while maintaining accuracy comparable to manual grading. The system combines automated scanning, multi-pass LLM scoring, consistency checks, and mandatory human verification to handle pen-and-paper tests at scale.
Key Takeaways
- →LLM-assisted grading reduces teacher workload by approximately 23% compared to fully manual grading.
- →The system maintains grading accuracy and fairness comparable to traditional manual methods through human oversight.
- →The workflow addresses the growing need for supervised in-class assessments as AI undermines take-home testing reliability.
- →Human verification and consistency checks effectively contain occasional LLM errors in the hybrid design.
- →The system was successfully deployed across six low-stakes mathematics tests in undergraduate courses.
#llm#education-ai#human-in-the-loop#automated-grading#mathematics#assessment#workflow-automation#ai-assisted-education
Read Original →via arXiv – CS AI
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