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 responses each, the study validates that AI-generated scores align moderately to highly with human raters across multiple rubric dimensions. The framework also identifies when human review is most needed, allowing more efficient allocation of expert effort while maintaining assessment quality.
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.
By Zewei Tian, Alex Liu, Lief Esbenshade, Michael Xiao, Zachary Zhang, Yulia L\'apicus, Thomas Han, Kevin He, Min Sun
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.
By Manuel Alonso-Carracedo, Ruben Fernandez-Boullon, Pedro Celard, Francisco J. Rodriguez-Martinez, Lorena Otero-Cerdeira
The paper introduces AICG, a two‑branch framework that enhances automated short answer grading by combining RoBERTa‑based answer scoring with a cognitive supervision branch that predicts skill mastery using a Q‑matrix and G‑DINA model. It also incorporates a generative‑AI detector to weight responses based on the likelihood of AI assistance, thereby reducing bias. Experiments on over 49,000 instructor‑graded responses show that AICG cuts mean absolute error by 50.3% and raises quadratic weighted kappa from 0.798 to 0.927 compared to baseline RoBERTa regression.
By Shengran Guo, Min Chi, Feifei Qiao, Varad Patwardhan
arXiv:2606. 06546v1 Announce Type: new Abstract: Evaluating large language models (LLMs) for education requires measuring how models teach, not only what they know.
By Tao Liu, Ye Lu, Ruohua Zhang, Siyu Song, Wentao Liu, Aimin Zhou, Hao Hao
arXiv:2504.02323v5 Announce Type: replace
Abstract: Large language models (LLMs) have created new opportunities to assist teachers and support student learning. While researchers have explored variou...
By Clayton Cohn, Ashwin T S, Naveeduddin Mohammed, Gautam Biswas