Generative AI for Scientific Illustration in Insect Taxonomy: A Human‐Supervised High‐Efficiency Workflow

Morphological illustrations are crucial in research on insect taxonomy and phylogenetics. Although traditional manual drawing and digital tracing techniques have undergone continuous evolution, they still face limitations such as low efficiency, heavy reliance on manual labour and high technical barriers. To explore more efficient illustration methodologies, this paper introduces a generative AI‐assisted approach, using Nano Banana as a case study and establishing a semi‐automated illustration pipeline suitable for insect morphology. Tests on habitus, wing venation and male genitalia images from two representative insect groups (Hemiptera and Coleoptera) showed that this pipeline offers significant advantages in drawing efficiency, generating an entire plate in 20–50 s (does not include post‐editing), which is far faster than traditional methods. Furthermore, the morphological accuracy and stylistic standardization of the outputs were sufficient for practical use in scientific publications after expert review under a human‐supervised workflow. These results suggest that AI‐assisted illustration could become a useful workflow for insect illustration in future taxonomic studies.