AIriskEval-edu: New Dataset for Risk Assessment in AI-mediated K-12 Educational Explanations
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...
| Authors | Javier Irigoyen, Roberto Daza, Francisco Jurado et al. |
|---|---|
| Primary category | cs.CL |
| Published | 2 July 2026 |
| Last updated at source | 2 July 2026 |
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