构建教师角色数据集，评估AI教学解释的潜在风险。
EduEVAL-DB: A Role-Based Dataset for Pedagogical Risk Evaluation in Educational Explanations
- 按真实教学风格设计六类教师角色，生成对比性教学解释。
- 标注854条解释的五维风险，含事实错误与意识形态偏见。
- 验证小模型在消费级设备上经微调后可识别教学风险。
本文提出EduEVAL-DB，一个基于教师角色的教学解释评估数据集，用于训练和评估自动教学评价系统与AI助教。数据集包含来自ScienceQA基准中139个问题的854条解释，覆盖K-12阶段的科学、语言与社会科学内容。每道题有1条真人教师解释和6条由大模型模拟不同教学角色生成的解释，这些角色基于真实教育实践中的教学风格与缺陷，通过提示工程实现。我们设计了一套符合教育标准的学业风险评分体系，涵盖五个互补维度：事实正确性、解释深度与完整性、重点与相关性、学生适切性及意识形态偏见。所有解释均通过半自动流程加专家教师审核标注二元风险标签。最后，我们进行初步验证实验，将教育专用模型Gemini 2.5 Pro与轻量级本地Llama 3.1 8B模型进行对比，检验在消费级硬件上对EduEVAL-DB进行监督微调是否能有效支持教学风险检测。
原文摘要 · Abstract (English)
This work introduces EduEVAL-DB, a dataset based on teacher roles designed to support the evaluation and training of automatic pedagogical evaluators and AI tutors for instructional explanations. The dataset comprises 854 explanations corresponding to 139 questions from a curated subset of the ScienceQA benchmark, spanning science, language, and social science across K-12 grade levels. For each question, one human-teacher explanation is provided and six are generated by LLM-simulated teacher roles. These roles are inspired by instructional styles and shortcomings observed in real educational practice and are instantiated via prompt engineering. We further propose a pedagogical risk rubric aligned with established educational standards, operationalizing five complementary risk dimensions: factual correctness, explanatory depth and completeness, focus and relevance, student-level appropriateness, and ideological bias. All explanations are annotated with binary risk labels through a semi-automatic process with expert teacher review. Finally, we present preliminary validation experiments to assess the suitability of EduEVAL-DB for evaluation. We benchmark a state-of-the-art education-oriented model (Gemini 2.5 Pro) against a lightweight local Llama 3.1 8B model and examine whether supervised fine-tuning on EduEVAL-DB supports pedagogical risk detection using models deployable on consumer hardware.
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