{"ID":5675261,"CreatedAt":"2026-07-03T01:40:09.565152011Z","UpdatedAt":"2026-07-07T01:06:03.009715918Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.01934","arxiv_id":"2607.01934","title":"AIriskEval-edu: New Dataset for Risk Assessment in AI-mediated K-12 Educational Explanations","abstract":"This work introduces AIriskEval-edu-db2, a new dataset designed to train and evaluate auditors based on LLMs for an explainable pedagogical risk assessment in instructional content for grades K-12. The dataset comprises 1,639 explanations from 170 curated ScienceQA questions, covering science, language arts, and social sciences. For each question, the dataset includes an explanation written by a human teacher alongside 11 explanations generated by LLM-simulated teacher profiles associated with distinct pedagogical risks. We propose a comprehensive risk rubric aligned with established educational standards that covers five complementary dimensions: factual precision, depth and completeness, focus and relevance, student-level appropriateness, and ideological bias. A key contribution is the addition of 785 explanations with structured explainability annotations, including risk localization and risk description. The annotations are produced through a semi-automatic process with expert teacher validation. Finally, we present validation experiments comparing state-of-the-art proprietary models with a lightweight local Llama 3.1 8B model in both the pedagogical risk detection and the explainability assessment. These experiments evaluate whether supervised fine-tuning on AIriskEval-edu-db2 enables a locally deployable model to approach or outperform stronger frontier models while preserving privacy in educational auditing and assessment tasks.","short_abstract":"This work introduces AIriskEval-edu-db2, a new dataset designed to train and evaluate auditors based on LLMs for an explainable pedagogical risk assessment in instructional content for grades K-12. The dataset comprises 1,639 explanations from 170 curated ScienceQA questions, covering science, language arts, and social...","url_abs":"https://arxiv.org/abs/2607.01934","url_pdf":"https://arxiv.org/pdf/2607.01934v1","authors":"[\"Javier Irigoyen\",\"Roberto Daza\",\"Francisco Jurado\",\"Julian Fierrez\",\"Ruben Tolosana\",\"Alvaro Ortigosa\",\"Enrique Blas\",\"Aythami Morales\"]","published":"2026-07-02T09:28:21Z","proceeding":"cs.CL","tasks":"[\"cs.CL\",\"cs.AI\",\"cs.DB\"]","methods":"[\"Large Language Model\"]","has_code":false}