Using AI-Assisted Coding to Build Digital Tools for Natural History Collections

Natural History Collections are invaluable sources of biological information for research, education, conservation, and public outreach but often lack the resources, programming expertise, and infrastructure needed to create custom digital tools for data accessibility. Large scale data aggregators facilitate data access but partially address common institutional needs. Artificial Intelligence (AI) has rapidly changed the way developers create, maintain, optimize, and test code, lowering the technical barrier to software creation. AI-assisted coding can accelerate the development of tools for digitization, data management and visualization, and public accessibility in Natural History Collections. Here I demonstrate the use of AI-assisted coding in the development of two custom web applications designed to address common institutional needs. Two web applications were built using GPT‑5.3‑Codex. Both were based on data exported from the Cornell University Museum of Vertebrates database. The first tool is a specimen search portal enabling multi‑field filtering, data export, and mapping of localities. The second contains an interactive map to visualize the sampling of localities for four collections and a cumulative graph of specimen and lot sampled through time. Both applications performed the intended functions accurately, demonstrating that AI‑assisted coding can be used to generate operational, lightweight applications for Natural History Collections. The development of web application through AI‑assisted coding reduces technical barriers for collections, enabling the creation of tailored tools with minimal infrastructure. Careful prompt design, refinement, and attention to data security are essential for robust results and safe adoption of this technology.