arXiv AI
ProIQA: A Process-Based Framework for Fine-Grained Math Item Quality Assessment
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Cognitive Episodes in LLM Reasoning Traces Enable Interpretable Human Item Difficulty Prediction
arXiv:2606. 28186v1 Announce Type: cross Abstract: Predicting human item difficulty is central to educational assessment, where reliable estimates support fairness and effective test construction.
arXiv AI
Jun 12
Constructing Evaluation Datasets for Procedural Reasoning: Balancing Naturalness, Grounding, and Multi-Hop Coverage
arXiv:2606. 12767v1 Announce Type: new Abstract: Evaluating procedural reasoning in AI-supported learning systems requires question-answer datasets that are both learner-like and grounded in the instructional knowledge the system is expected to use.
arXiv Computation and Language
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RAWR: Reward Assignment Without Rollouts in Verifiable Domains
arXiv:2603.17815v2 Announce Type: replace Abstract: Understanding and evaluating multi-step reasoning in LLMs at the level of individual steps remains a key challenge. Process reward models (PRMs) pr...
arXiv AI
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Evolving from Lessons: Skill-Augmented Table Graph Reasoning for Operation-wise Table Question Answering
arXiv:2607. 22633v1 Announce Type: new Abstract: Table Question Answering (TableQA) aims to reason over tables to answer user queries.
arXiv Machine Learning
Jun 8
Elmes*: Automated Construction of Fine-Grained Evaluation Rubrics for Large Language Models in Long-Tail Educational Scenarios
arXiv:2606. 06546v1 Announce Type: new Abstract: Evaluating large language models (LLMs) for education requires measuring how models teach, not only what they know.
arXiv AI
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Creating and Evaluating K-12 GenAI Assessment Graders Through Context Engineering
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.