Multimodal Large Language Models (MLLMs) show remarkable progress across many visual-language tasks; however, their capacity to evaluate artistic expression remains limited. Aesthetic concepts are inherently abstract and open-ended, and multimodal artwork annotations are scarce. We introduce KidsArtBench, a new benchmark of over 1k children's artworks (ages 5-15) annotated by 12 expert educators across 9 rubric-aligned dimensions, together with expert comments for feedback. Unlike prior aesthetic datasets that provide single scalar scores on adult imagery, KidsArtBench targets children's artwork and pairs multi-dimensional annotations with comment supervision to enable both ordinal assessment and formative feedback. Building on this resource, we propose an attribute-specific multi-LoRA approach, where each attribute corresponds to a distinct evaluation dimension (e.g., Realism, Imagination) in the scoring rubric, with Regression-Aware Fine-Tuning (RAFT) to align predictions with ordinal scales. On Qwen2.5-VL-7B, our method increases correlation from 0.468 to 0.653, with the largest gains on perceptual dimensions and narrowed gaps on higher-order attributes. These results show that educator-aligned supervision and attribute-aware training yield pedagogically meaningful evaluations and establish a rigorous testbed for sustained progress in educational AI. We release data and code with ethics documentation.

翻译：多模态大语言模型（MLLMs）在众多视觉-语言任务中展现出显著进展，但其评估艺术表达的能力仍然有限。审美概念本质上是抽象且开放式的，而多模态艺术作品标注数据稀缺。我们提出了KidsArtBench，这是一个包含1000余幅儿童艺术作品（年龄5-15岁）的新基准数据集，由12位专家教育工作者依据9个评分维度进行标注，并附有专家评语作为反馈。与先前仅对成人图像提供单一标量评分的美学数据集不同，KidsArtBench专注于儿童艺术作品，并将多维度标注与评语监督相结合，以支持序数评估和形成性反馈。基于此资源，我们提出了一种属性特定的多LoRA方法，其中每个属性对应评分量表中的不同评估维度（如写实性、想象力），并采用回归感知微调（RAFT）使预测与序数尺度对齐。在Qwen2.5-VL-7B模型上，我们的方法将相关性从0.468提升至0.653，在感知维度上提升最为显著，并在高阶属性上缩小了差距。这些结果表明，与教育工作者对齐的监督和属性感知训练能够产生具有教育学意义的评估，并为教育人工智能的持续进步建立了严谨的测试平台。我们已发布数据、代码及伦理文档。