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.

翻译：本研究提出并评估了生成式人工智能（AI）在围绕潜在清洁能源引发公众参与方面的潜力。此类应用有望提升能源素养——即公众对低碳能源的认知，进而促进其更积极地参与能源系统未来的决策。我们探索了利用生成式AI向公众传达低碳能源技术信息的应用，特别聚焦于核能领域。通过考察20款基于AI的文本到图像生成器，我们比较了它们在通用及科学核能相关提示词上的个体表现。其中，DALL-E、DreamStudio和Craiyon三款模型在根据核能主题通用文本生成相关图像方面展现出可观性能。然而，这些模型在三个关键层面存在不足：（1）无法准确呈现能源系统的技术细节；（2）再现了能源领域围绕性别与工作的既有偏见；（3）未能准确描绘本土景观——这些地区历来是能源产业资源开采与废物沉积的场所。本研究旨在推动专用生成工具及其注释的开发，以提升能源素养，并有效引导公众关注低碳能源。