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    <abstract>Multimodal Large Language Models (MLLMs) show 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. Our 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.</abstract>
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%0 Conference Proceedings
%T KidsArtBench: Multi-Dimensional Children’s Art Evaluation with Attribute-Aware MLLMs
%A Ye, Mingrui
%A Zheng, Chanjin
%A Yu, Zengyi
%A Xiang, Chenyu
%A Zhao, Zhixue
%A Yuan, Zheng
%A Yannakoudakis, Helen
%Y Demberg, Vera
%Y Inui, Kentaro
%Y Marquez, Lluís
%S Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-380-7
%F ye-etal-2026-kidsartbench
%X Multimodal Large Language Models (MLLMs) show 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. Our 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.
%U https://aclanthology.org/2026.eacl-long.267/
%P 5702-5722

Markdown (Informal)

[KidsArtBench: Multi-Dimensional Children’s Art Evaluation with Attribute-Aware MLLMs](https://aclanthology.org/2026.eacl-long.267/) (Ye et al., EACL 2026)

• KidsArtBench: Multi-Dimensional Children’s Art Evaluation with Attribute-Aware MLLMs (Ye et al., EACL 2026)

ACL

• Mingrui Ye, Chanjin Zheng, Zengyi Yu, Chenyu Xiang, Zhixue Zhao, Zheng Yuan, and Helen Yannakoudakis. 2026. KidsArtBench: Multi-Dimensional Children’s Art Evaluation with Attribute-Aware MLLMs. In Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), pages 5702–5722, Rabat, Morocco. Association for Computational Linguistics.