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 education
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
- Linked via arxiv authorJavier Irigoyen →
AIriskEval-edu: New Dataset for Risk Assessment in AI-mediated K-12 Educational Explanations
- Linked via arxiv authorRoberto Daza →
AIriskEval-edu: New Dataset for Risk Assessment in AI-mediated K-12 Educational Explanations
- Linked via arxiv authorFrancisco Jurado →
AIriskEval-edu: New Dataset for Risk Assessment in AI-mediated K-12 Educational Explanations
- Linked via arxiv authorJulian Fierrez →
AIriskEval-edu: New Dataset for Risk Assessment in AI-mediated K-12 Educational Explanations
- Linked via arxiv authorRuben Tolosana →
AIriskEval-edu: New Dataset for Risk Assessment in AI-mediated K-12 Educational Explanations
- Linked via arxiv authorAlvaro Ortigosa →
AIriskEval-edu: New Dataset for Risk Assessment in AI-mediated K-12 Educational Explanations
- Linked via arxiv authorEnrique Blas →
AIriskEval-edu: New Dataset for Risk Assessment in AI-mediated K-12 Educational Explanations
- Linked via arxiv authorAythami Morales →
AIriskEval-edu: New Dataset for Risk Assessment in AI-mediated K-12 Educational Explanations