EduResearchBench: 一个用于全生命周期教育研究的分层原子任务分解基准
EduResearchBench: A Hierarchical Atomic Task Decomposition Benchmark for Full-Lifecycle Educational Research
AI总结：
EduResearchBench通过分层原子任务分解框架，构建了首个教育学术写作评估平台，展示了在垂直领域中数据质量和分层训练的重要性。
摘要：
尽管大型语言模型（LLMs）正在重塑人工智能用于社会科学（AI4SS）的范式，但严格评估其在学术写作中的能力仍然是一个重大挑战。现有基准主要强调单次生成，因此缺乏反映复杂学术研究流程的细粒度评估。为填补这一空白，我们引入了EduResearchBench，这是首个专门用于教育学术写作的综合评估平台。EduResearchBench基于我们提出的分层原子任务分解（HATD）框架，将端到端的研究流程分解为六个专门的研究模块（例如定量分析、定性研究和政策研究），涵盖24个细粒度的原子任务。这种分类使能够实现自动化评估流程，从而弥补了整体评分的限制，即汇总分数往往掩盖了特定能力瓶颈，而提供针对具体缺陷的细粒度、诊断性反馈。此外，认识到学术写作中的高认知负荷，我们提出了一种课程学习策略，从基础技能逐步构建到复杂的推理和论证。利用55,000个原始学术样本，我们整理了11,000对高质量的指令对来训练EduWrite，一个专门用于教育学术写作的模型。实验表明，EduWrite（30B）在多个核心指标上显著优于更大的通用模型（72B），证明在垂直领域中，数据质量密度和分层训练课程比参数规模更重要。
英文摘要：
While Large Language Models (LLMs) are reshaping the paradigm of AI for Social Science (AI4SS), rigorously evaluating their capabilities in scholarly writing remains a major challenge. Existing benchmarks largely emphasize single-shot, monolithic generation and thus lack the fine-grained assessments required to reflect complex academic research workflows. To fill this gap, we introduce EduResearchBench, the first comprehensive evaluation platform dedicated to educational academic writing. EduResearchBench is built upon our Hierarchical Atomic Task Decomposition (HATD) framework, which decomposes an end-to-end research workflow into six specialized research modules (e.g., Quantitative Analysis, Qualitative Research, and Policy Research) spanning 24 fine-grained atomic tasks. This taxonomy enables an automated evaluation pipeline that mitigates a key limitation of holistic scoring, where aggregate scores often obscure specific capability bottlenecks, and instead provides fine-grained, diagnostic feedback on concrete deficiencies. Moreover, recognizing the high cognitive load inherent in scholarly writing, we propose a curriculum learning strategy that progressively builds competence from foundational skills to complex methodological reasoning and argumentation. Leveraging 55K raw academic samples, we curate 11K high-quality instruction pairs to train EduWrite, a specialized educational scholarly writing model. Experiments show that EduWrite (30B) substantially outperforms larger general-purpose models (72B) on multiple core metrics, demonstrating that in vertical domains, data quality density and hierarchically staged training curricula are more decisive than parameter scale.
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