CLARA框架用1107篇中英双语儿童故事评测AI发展适宜性判断
CLARA: Can AI Assess Developmental Appropriateness in Children's Stories?
研究者做了个CLARA框架，拿1107篇中英儿童故事测AI能不能像教育者一样判断故事适不适合孩子，结果是结构化标注确实有效。
arXiv 论文提出 CLARA，一个面向儿童叙事发展理解的认知框架，覆盖认知（COG）、语言（LAN）、社会情感（SEL）三个维度。配套发布 1107 篇中英双语儿童故事基准，含标准化 silver 发展参照与结构化标注。实验对比显示，结构化发展标注相比基于可读性的方法和直接提示基线，与人类教育者盲评的一致性明显更强。
CLARA: Can AI Assess Developmental Appropriateness in Children's Stories?
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.
