arXiv Machine Learning June 9, 2026 By Gabriel Ortega, Abelino Jim\'enez, S\'everin Lions, Pablo Dartnell

Structure-Aware Modeling of Multiple-Choice Questions Improves Automatic Difficulty Estimation

Read the original on arXiv Machine Learning →

arXiv:2606. 08988v1 Announce Type: cross Abstract: Automatic Question Difficulty Estimation (AQDE) holds growing promise for educational assessment because it has the potential to yield difficulty estimates that are competitive with expert judgment, while helping reduce the time and financial burden associated with pilot administrations and scaling to digital testing contexts.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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Cognitive Episodes in LLM Reasoning Traces Enable Interpretable Human Item Difficulty Prediction

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