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.

翻译：诊断推理能力的培养需要基于多样化的学生作品进行练习，然而隐私法规限制了在规模化教师专业发展（PD）中共享真实学生作品。我们提出了DrawSim-PD，首个生成式框架，用于模拟符合NGSS标准、呈现可控教学缺陷的学生风格科学绘图，以支持教师培训。我们方法的核心是能力档案——一种结构化的认知状态编码，描述每个表现水平的学生能够展示和尚未能展示的内容。这些档案确保了生成输出之间的跨模态一致性：（i）学生风格绘图，（ii）第一人称推理叙述，以及（iii）面向教师的诊断概念图。基于100个涵盖K-12年级的精选NGSS主题，我们构建了包含10,000个系统化结构作品的语料库。通过专家主导的可行性评估，K-12科学教育者验证了作品与NGSS期望的一致性（核心项目认可度>84%）及其在解读学生思维方面的实用性，同时指出了针对年级段极值作品的改进空间。我们开源此基础设施，以突破视觉评估研究中的数据稀缺障碍。