Can we derive computational metrics to quantify visual creativity in drawings across intelligent agents, while accounting for inherent differences in technical skill and style? To answer this, we curate a novel dataset consisting of 1338 drawings by children, adults and AI on a popular creative drawing task. We characterize two aspects of the drawings—(1) style and (2) content. For style, we define measures based on ink density, ink distribution and number of elements. For content, we use expert-annotated categories to study conceptual diversity, and image and text embeddings to compute distance measures. We find significant differences in style and content in the groups—children’s drawings had more components, AI drawings had greater ink density, and adult drawings were conceptually diverse. We also highlight a misalignment between creativity judgments obtained through expert and automated ratings. Our work provides a novel framework for studying human and artificial creativity beyond the textual modality.