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arXiv:2606. 12422v1 Announce Type: cross Abstract: The integration of large language models (LLMs) into educational assessment represents a transformative shift in classroom grading practices.
arXiv:2607. 02432v1 Announce Type: new Abstract: Scalable and reliable grading of command-line examinations remains a challenge in computing education, where rising enrolments make manual marking difficult and rule-based autograders cannot handle partial credit, equivalent solutions, or syntactic variation.
arXiv:2606. 18257v1 Announce Type: cross Abstract: While LLMs show promise in automating educational content creation, their ability to generate questions that stimulate higher-order thinking remains understudied.
arXiv:2607. 01247v1 Announce Type: cross Abstract: Open-ended mathematics exams are valuable because they assess reasoning, proof construction, algorithmic thinking, and communication of intermediate steps.
arXiv:2509.05346v3 Announce Type: replace Abstract: While large language models (LLMs) are increasingly being adopted to support personalized learning, there remains limited understanding of how thei...
The paper introduces a human‑in‑the‑loop framework for AI‑assisted scoring of short written responses in a large‑scale national assessment. Using data from two recent test editions with about 5,000 student responses each, the authors validate that AI‑generated scores align moderately to highly with human raters across multiple rubric dimensions. The framework includes a correction workflow that flags cases needing human review, thereby reducing manual workload while maintaining assessment quality.