AIriskEval-edu: New Dataset for Risk Assessment in AI-mediated K-12 Educational Explanations
arXiv:2607.01934v1 Announce Type: new 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 comprehensiv
The proliferation of LLMs in educational settings necessitates immediate development of robust risk assessment tools to ensure their safe and effective deployment.
Evaluating the pedagogical risks of AI-generated content in K-12 education is critical for managing the quality and safety of foundational learning experiences, directly impacting future human capital.
The introduction of AIriskEval-edu provides a standardized dataset for training and evaluating AI auditors, enabling more systematic management of risks associated with LLM explanations in education.
- · AI safety researchers
- · Educational technology providers
- · K-12 students (indirect)
- · AI auditors
- · Unregulated AI explanation tools
- · Educational institutions without AI governance
- · LLMs generating low-quality educational content
The dataset enables better identification and mitigation of pedagogical risks in AI-mediated educational content.
Improved AI safety standards in education could lead to wider adoption of AI tools in K-12 with greater trust from educators and parents.
The development of specialized AI auditors for education may become a new, critical professional field, influencing curriculum design and pedagogical practices.
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