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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- PossiblePossibly related (embedding) · 47%How Preply combines AI and human tutors to personalize learning →
- PossiblePossibly related (embedding) · 47%edefbo1/a-hi →
- LinkedLinked via arxiv author · 85%Javier Irigoyen →
“AIriskEval-edu: New Dataset for Risk Assessment in AI-mediated K-12 Educational Explanations”
- LinkedLinked via arxiv author · 85%Roberto Daza →
“AIriskEval-edu: New Dataset for Risk Assessment in AI-mediated K-12 Educational Explanations”
- LinkedLinked via arxiv author · 85%Francisco Jurado →
“AIriskEval-edu: New Dataset for Risk Assessment in AI-mediated K-12 Educational Explanations”
- LinkedLinked via arxiv author · 85%Julian Fierrez →
“AIriskEval-edu: New Dataset for Risk Assessment in AI-mediated K-12 Educational Explanations”
- LinkedLinked via arxiv author · 85%Ruben Tolosana →
“AIriskEval-edu: New Dataset for Risk Assessment in AI-mediated K-12 Educational Explanations”
- LinkedLinked via arxiv author · 85%Alvaro Ortigosa →
“AIriskEval-edu: New Dataset for Risk Assessment in AI-mediated K-12 Educational Explanations”