发表机构
BiometricsAI, Universidad Autónoma de Madrid (UAM); GHIA, Universidad Autónoma de Madrid (UAM); Universidad de Las Palmas de Gran Canaria (ULPGC)（生物识别人工智能，马德里自治大学（UAM）; GHIA，马德里自治大学（UAM）; 大加那利岛拉斯帕尔马斯大学（ULPGC））
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
AIriskEval-edu演示平台可审计教学解释质量，依据五个维度评分标准评估，集成GPT-5.5和自托管评估器，有两种运行模式，本地评估器在多数指标上优于GPT-5.5，为教育机构提供实用审计方法。
AI 中文摘要
我们展示了AIriskEval-edu演示平台，它可审计教学解释的教学质量并提供可解释的审计结果。该平台依据涵盖教学风险五个维度的评分标准评估解释，对每个维度返回二元决策和置信度得分，还包括自然语言理由及证据范围。它通过外部API集成GPT-5.5和在消费级GPU上运行的自托管Llama 3.1 8B评估器，在AIriskEval-edu数据集上微调。平台有两种模式，在AI模式下评估模拟教师生成的解释，在人类模式下实时审计用户编写的解释。本地评估器在多数指标上优于GPT-5.5，为教育机构提供了在自身基础设施内审计内容的实用方法。
英文摘要
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.
Comments6 pages, 2 figures. Accepted at the 17th IAPR International Workshop on Document Analysis Systems (DAS 2026), ICDAR 2026, September 3, 2026
