用生成模型模拟学生科学绘图，助教师训练诊断能力。
DrawSim-PD: Simulating Student Science Drawings to Support NGSS-Aligned Teacher Diagnostic Reasoning
- 构建能力画像，生成符合NGSS标准的带教学缺陷绘图
- 产出1万份结构化绘图、反思叙事与诊断地图
- 适合教师培训，解决真实学生作品无法共享难题
培养诊断推理能力需大量接触学生作品，但隐私限制阻碍了教师专业发展中的大规模使用。我们提出DrawSim-PD，首个生成式框架，可生成符合NGSS标准、带有可控教学缺陷的学生风格科学绘图，支持教师培训。核心是能力画像——结构化认知状态，描述不同水平学生能或不能展示的内容，确保生成结果在跨模态间的一致性：（i）类学生绘图，（ii）第一人称推理叙述，（iii）教师可用的诊断概念图。基于100个覆盖K-12的NGSS主题，构建了1万份系统化生成物。专家评估显示，超过84%的核心项目符合NGSS要求，且对理解学生思维有帮助，仅在学段极端情况需优化。该开放基础设施旨在克服视觉评估研究中的数据稀缺问题。
原文摘要 · Abstract (English)
Developing expertise in diagnostic reasoning requires practice with diverse student artifacts, yet privacy regulations prohibit sharing authentic student work for teacher professional development (PD) at scale. We present DrawSim-PD, the first generative framework that simulates NGSS-aligned, student-like science drawings exhibiting controllable pedagogical imperfections to support teacher training. Central to our approach are apability profiles--structured cognitive states encoding what students at each performance level can and cannot yet demonstrate. These profiles ensure cross-modal coherence across generated outputs: (i) a student-like drawing, (ii) a first-person reasoning narrative, and (iii) a teacher-facing diagnostic concept map. Using 100 curated NGSS topics spanning K-12, we construct a corpus of 10,000 systematically structured artifacts. Through an expert-based feasibility evaluation, K--12 science educators verified the artifacts' alignment with NGSS expectations (>84% positive on core items) and utility for interpreting student thinking, while identifying refinement opportunities for grade-band extremes. We release this open infrastructure to overcome data scarcity barriers in visual assessment research.
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