Dietary transitions away from animal-based meat could reduce food-related environmental burdens, but plant-based alternatives must compete on flavor, texture, and overall liking. Here we extend a generative AI framework with 469 plant-based burger recipes and use it to create six formulations across five primary ingredients: bean, chickpea, lentil, mushroom, and oat. An executive chef translates the AI-generated formulations into standardized preparation protocols and refines the ingredient quantities based on culinary expertise. The integration of generative modeling and culinary expertise defines a human-centered generative AI framework for food design. We evaluate the six burgers in a blinded sensory survey with 101 participants in an operating restaurant and quantify their land use, greenhouse gas emissions, eutrophication potential, and scarcity-weighted water use. We use Big Mac scores from a previous survey as an iconic commercial benchmark. The mushroom burger achieves mean flavor, texture, and overall liking scores similar to the Big Mac, with less than one-third of its estimated environmental impact. The other formulations reduce the estimated impact further, in several cases to near one-tenth of the Big Mac value, but receive lower liking scores. Mixed-effects models quantify associations between sensory attributes and human liking and retain predictive accuracy on independent survey data, with mean absolute errors no greater than 0.52 points on a seven-point scale. Together, these results demonstrate how generative design, culinary expertise, human sensory response, and environmental assessment can converge to create sustainable foods that people want to eat.

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