ESQAD: A curriculum-aligned dataset for question answer generation in Spanish

This paper presents ESQAD (Educational Spanish Question-Answer Dataset), an open-access resource for question-answer generation (QAG) in Spanish aligned with national curricula. ESQAD comprises 6,980 expert-curated exam questions, 340 literary comprehension items, 47 legal FAQs, and 913 LLM-generated pairs validated in academic settings. The dataset covers diverse linguistic and cognitive features, including subject classification, question typologies, and Bloom-level complexity. We analyze lexical diversity, cognitive intent, and structure across subsets. The automatically generated portion was created with Bloom-guided prompts and large language models (LLMs), and evaluated through both computational metrics and classroom validation. Results show that cognitive-level control is reliable for factual recall but only partial for higher-order reasoning. All resources, including dataset, code, and validation tool, are publicly available. ESQAD provides a structured benchmark for curriculum-aware QAG in under-resourced languages, with applications in educational NLP, question generation, and adaptive learning.