发表机构
University of Auckland（奥克兰大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出CLARA框架，通过结构化注释评估儿童故事的发展适宜性，实验证明其优于可读性方法，并强调可解释性与人类监督的重要性。
AI 中文摘要
评估儿童叙事的发展适宜性对于教育推荐和发展性读写研究具有重要意义，然而此类评估通常依赖于主观且难以规模化的人类判断。这引出了一个重要问题：AI系统能否近似人类对儿童故事的发展性判断？为研究这一问题，我们提出了CLARA，一个基于认知的框架，通过结构化注释在认知（COG）、语言（LAN）和社会情感（SEL）三个维度上进行发展性叙事理解，并附带一个包含1107个中英双语儿童故事的双语基准资源，其中包含归一化的银标准发展参考和结构化发展注释。我们通过基准比较、组件分析、翻译双语一致性分析以及教育者的盲人评估来评估CLARA。实验结果表明，结构化发展注释在与发展参考和人类判断的对齐程度上显著强于基于可读性的方法和直接提示基线。总体而言，我们的研究结果表明，在结构化发展注释的引导下，AI系统能够近似人类发展性判断的某些方面，同时也强调了在教育自然语言处理中可解释性和人类监督的重要性。
英文摘要
Assessing the developmental suitability of children's narratives is important for educational recommendation and developmental literacy research, yet such assessment typically relies on subjective and difficult-to-scale human judgment. This raises an important question: Can AI systems approximate human developmental judgments of children's stories? To study this problem, we introduce CLARA, a cognitively grounded framework for developmental narrative understanding through structured annotation across cognitive (COG), language (LAN), and social-emotional (SEL) dimensions, together with a bilingual benchmark resource containing 1107 Chinese--English children's stories with normalized silver developmental references and structured developmental annotations. We evaluate CLARA through benchmark comparison, component analysis, translated bilingual consistency analysis, and blinded human evaluation with educators. Experimental results show that structured developmental annotation achieves substantially stronger alignment with developmental references and human judgments than readability-based methods and direct prompting baselines. Overall, our findings suggest that AI systems can approximate certain aspects of human developmental judgment when guided by structured developmental annotation, while also highlighting the importance of interpretability and human oversight in educational NLP.
CommentsAccepted to Findings of EMNLP 2026
