AIriskEval-edu Demo: Auditing of Pedagogical Risks in Educational Explanations

Abstract:We present AIriskEval-edu Demo, a platform that audits the pedagogical quality of instructional explanations and provides explainable audit results. The platform evaluates an explanation against a rubric covering five dimensions of pedagogical risk: factual accuracy, depth and completeness, focus and relevance, student-level appropriateness, and ideological bias. For each dimension, it returns a binary decision and a confidence score. Detected risks also include a natural-language rationale and, except for Depth and Completeness, a localized evidence span. The platform integrates GPT-5.5 through an external API and a self-hosted Llama 3.1 8B evaluator that runs on consumer-grade GPUs. The local evaluator is fine-tuned on AIriskEval-edu, a dataset of K-12 instructional explanations with risk and explainability annotations. The platform operates in two modes: in AI mode, both evaluators assess stored explanations generated under six simulated teacher profiles, each representing a distinct pedagogical behavior and potential risk; in human mode, the local evaluator audits user-written explanations in real time. The local evaluator outperforms GPT-5.5 on most reported metrics, offering educational institutions a practical way to keep audited content within their own infrastructure.

6 pages, 2 figures. Accepted at the 17th IAPR International Workshop on Document Analysis Systems (DAS 2026), ICDAR 2026, September 3, 2026

Artificial Intelligence (cs.AI); Computation and Language (cs.CL)

arXiv:2607.25634 [cs.AI]

(or arXiv:2607.25634v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2607.25634 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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