AI-native biodesigner: a definition and a three-question test for biodesign education

AI models now take part in many kinds of biodesign. They predict protein shapes, suggest changes to enzymes, steer how grown living matter forms and choose the next experiment in automated labs. Some of this takes minutes. That lowers the barrier to designing with living matter, but it also raises a question for education: what does a good biodesigner need when the tools are this fast? This paper proposes the term AI-native biodesigner for a person, not a tool. An AI-native biodesigner is a designer of living matter who works with AI models throughout a project, reads what their outputs cannot show, and asks what a change would mean for the organisms and people involved. The definition has three parts. Judgement is the anchor: saying what a model's output shows, what it cannot show, and what test with the living matter would check it. Fluency supports it: running the tools without coding or a lab, and knowing their inputs, outputs and costs. Care completes it: naming who and what a change touches before making it. Judgement and care are two halves of one skill, reading what a model leaves out. The term is openly evaluative. It describes doing the work well, not using AI a lot. To make this precise, the paper separates two things that are often run together: where the models sit in the work, and how their outputs are read. Crossing the two gives three ways of working, AI-added, AI-led and AI-native, set out in a simple two-by-two. The paper traces where the adjective "AI-native" comes from, reports a prior-art search that found no earlier definition of the term for a person, and offers a three-question test that teachers can use as a learning outcome and a marking prompt, with weak and strong answers from a worked example. It closes with the limits of the term, including criticism of the word "native" itself.