  In this work, we propose and assess the potential of generative artificial
intelligence (AI) to generate public engagement around potential clean energy
sources. Such an application could increase energy literacy -- an awareness of
low-carbon energy sources among the public therefore leading to increased
participation in decision-making about the future of energy systems. We explore
the use of generative AI to communicate technical information about low-carbon
energy sources to the general public, specifically in the realm of nuclear
energy. We explored 20 AI-powered text-to-image generators and compared their
individual performances on general and scientific nuclear-related prompts. Of
these models, DALL-E, DreamStudio, and Craiyon demonstrated promising
performance in generating relevant images from general-level text related to
nuclear topics. However, these models fall short in three crucial ways: (1)
they fail to accurately represent technical details of energy systems; (2) they
reproduce existing biases surrounding gender and work in the energy sector; and
(3) they fail to accurately represent indigenous landscapes -- which have
historically been sites of resource extraction and waste deposition for energy
industries. This work is performed to motivate the development of specialized
generative tools and their captions to improve energy literacy and effectively
engage the public with low-carbon energy sources.
