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DrawSim-PD: Simulating Student Science Drawings to Support NGSS-Aligned Teacher Diagnostic Reasoning
Conference paper

DrawSim-PD: Simulating Student Science Drawings to Support NGSS-Aligned Teacher Diagnostic Reasoning

Arijit Chakma, Peng He, Zeyuan Wang, Honglu Liu, Tingting Li, Tiffany D. Do and Feng Liu
Artificial Intelligence in Education, pp 17-32
27 Jun 2026
DOI: https://doi.org/10.1007/978-3-032-29744-0_2

Abstract

formative assessment generative AI K–12 science education NGSS student drawings teacher diagnostic reasoning
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 capability 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. This infrastructure addresses data scarcity barriers in visual assessment research. Project

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