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In this work, we propose and assess the potential of generative artificialintelligence (AI) to generate public engagement around potential clean energysources. Such an application could increase energy literacy -- an awareness oflow-carbon energy sources among the public therefore leading to increasedparticipation in decision-making about the future of energy systems. We explorethe use of generative AI to communicate technical information about low-carbonenergy sources to the general public, specifically in the realm of nuclearenergy. We explored 20 AI-powered text-to-image generators and compared theirindividual performances on general and scientific nuclear-related prompts. Ofthese models, DALL-E, DreamStudio, and Craiyon demonstrated promisingperformance in generating relevant images from general-level text related tonuclear topics. However, these models fall short in three crucial ways: (1)they fail to accurately represent technical details of energy systems; (2) theyreproduce existing biases surrounding gender and work in the energy sector; and(3) they fail to accurately represent indigenous landscapes -- which havehistorically been sites of resource extraction and waste deposition for energyindustries. This work is performed to motivate the development of specializedgenerative tools and their captions to improve energy literacy and effectivelyengage the public with low-carbon energy sources.

A Comparative Analysis of Text-to-Image Generative AI Models in Scientific Contexts: A Case Study on Nuclear Power