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Generative models, such as DALL-E 2 (OpenAI), could represent promising future tools for image generation, augmentation, and manipulation for artificial intelligence research in radiology, provided that these models have sufficient medical domain knowledge. Herein, we show that DALL-E 2 has learned relevant representations of x-ray images, with promising capabilities in terms of zero-shot text-to-image generation of new images, the continuation of an image beyond its original boundaries, and the removal of elements; however, its capabilities for the generation of images with pathological abnormalities (eg, tumors, fractures, and inflammation) or computed tomography, magnetic resonance imaging, or ultrasound images are still limited. The use of generative models for augmenting and generating radiological data thus seems feasible, even if the further fine-tuning and adaptation of these models to their respective domains are required first.

The proliferation of generative artificial intelligence (GenAI) means we are witnessing transformative change in education. While GenAI offers exciting possibilities for personalised learning and innovative teaching methodologies, its potential for reinforcing biases and perpetuating stereotypes poses ethical and pedagogical concerns. This article aims to critically examine the images produced by the integration of DALL-E 3 and ChatGPT, focusing on representations of science classrooms and educators. Applying a capital lens, we analyse how these images portray forms of culture (embodied, objectified and institutionalised) and explore if these depictions align with, or contest, stereotypical representations of science education. The science classroom imagery showcased a variety of settings, from what the GenAI described as vintage to contemporary. Our findings reveal the presence of stereotypical elements associated with science educators, including white-lab coats, goggles and beakers. While the images often align with stereotypical views, they also introduce elements of diversity. This article highlights the importance for ongoing vigilance about issues of equity, representation, bias and transparency in GenAI artefacts. This study contributes to broader discourses about the impact of GenAI in reinforcing or dismantling stereotypes associated with science education.

OpenAI and ChatGPT, as state-of-the-art language models driven by cutting-edge artificial intelligence technology, have gained widespread adoption across diverse industries. In the realm of computer vision, these models have been employed for intricate tasks including object recognition, image generation, and image processing, leveraging their advanced capabilities to fuel transformative breakthroughs. Within the gaming industry, they have found utility in crafting virtual characters and generating plots and dialogues, thereby enabling immersive and interactive player experiences. Furthermore, these models have been harnessed in the realm of medical diagnosis, providing invaluable insights and support to healthcare professionals in the realm of disease detection. The principal objective of this paper is to offer a comprehensive overview of OpenAI, OpenAI Gym, ChatGPT, DALL E, stable diffusion, the pre-trained clip model, and other pertinent models in various domains, encompassing CLIP Text-to-Image, education, medical imaging, computer vision, social influence, natural language processing, software development, coding assistance, and Chatbot, among others. Particular emphasis will be placed on comparative analysis and examination of popular text-to-image and text-to-video models under diverse stimuli, shedding light on the current research landscape, emerging trends, and existing challenges within the domains of OpenAI and ChatGPT. Through a rigorous literature review, this paper aims to deliver a professional and insightful overview of the advancements, potentials, and limitations of these pioneering language models.

In this work, we propose and assess the potential of generative artificial intelligence (AI) as a tool for facilitating 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 to improve energy literacy and effectively engage the public with low-carbon energy sources.

This study explores how contemporary text-to-image models interpret and generate Classical and Baroque styles under Wölfflin’s framework—two categories that are atemporal and transversal across media. Our goal is to see whether generative AI can replicate the nuanced stylistic cues that art historians attribute to them. We prompted two popular models (DALL•E and Midjourney) using explicit style labels (e.g., “baroque” and “classical”) as well as more implicit cues (e.g., “dynamic”, “static”, or reworked Wölfflin descriptors). We then collected expert ratings and conducted broader qualitative reviews to assess how each output aligned with Wölfflin’s characteristics. Our findings suggest that the term “baroque” usually evokes features recognizable in typically historical Baroque artworks, while “classical” often yields less distinct results, particularly when a specified genre (portrait, still life) imposes a centered, closed-form composition. Removing explicit style labels may produce highly abstract images, revealing that Wölfflin’s descriptors alone may be insufficient to convey Classical or Baroque styles efficiently. Interestingly, the term “dynamic” gives rather chaotic images, yet this chaos is somehow ordered, centered, and has an almost Classical feel. Altogether, these observations highlight the complexity of bridging canonical stylistic frameworks and contemporary AI training biases, underscoring the need to update or refine Wölfflin’s atemporal categories to accommodate how generative models—and modern visual culture—reinterpret Classical and Baroque.

The rapid advancements in large language models (LLMs) such as ChatGPT have raised concerns about their potential impact on academic integrity. While initial concerns focused on ChatGPT’s writing capabilities, recent updates have integrated DALL-E 3’s image generation features, extending the risks to visual evidence in biomedical research. Our tests revealed ChatGPT’s nearly barrier-free image generation feature can be used to generate experimental result images, such as blood smears, Western Blot, immunofluorescence and so on. Although the current ability of ChatGPT to generate experimental images is limited, the risk of misuse is evident. This development underscores the need for immediate action. We suggest that AI providers restrict the generation of experimental image, develop tools to detect AI-generated images, and consider adding “invisible watermarks” to the generated images. By implementing these measures, we can better ensure the responsible use of AI technology in academic research and maintain the integrity of scientific evidence.

PurposeThe paper aims to expand on the works well documented by Joy Boulamwini and Ruha Benjamin by expanding their critique to the African continent. The research aims to assess if algorithmic biases are prevalent in DALL-E 2 and Starry AI. The aim is to help inform better artificial intelligence (AI) systems for future use.Design/methodology/approachThe paper utilised a desktop study for literature and gathered data from Open AI’s DALL-E 2 text-to-image generator and StarryAI text-to-image generator.FindingsThe DALL-E 2 significantly underperformed when it was tasked with generating images of “An African Family” as opposed to images of a “Family”. The pictures lacked any conceivable detail as compared to the latter of this comparison. The StarryAI significantly outperformed the DALL-E 2 and rendered visible faces. However, the accuracy of the culture portrayed was poor.Research limitations/implicationsBecause of the chosen research approach, the research results may lack generalisability. Therefore, researchers are encouraged to test the proposed propositions further. The implications, however, are that more inclusion is warranted to help address the issue of cultural inaccuracies noted in a few of the paper’s experiments.Practical implicationsThe paper is useful for advocates who advocate for algorithmic equality and fairness by highlighting evidence of the implications of systemic-induced algorithmic bias.Social implicationsThe reduction in offensive racism and more socially appropriate AI can be a better product for commercialisation and general use. If AI is trained on diversity, it can lead to better applications in contemporary society.Originality/valueThe paper’s use of DALL-E 2 and Starry AI is an under-researched area, and future studies on this matter are welcome.

Educators and students have shown significant interest in the potential for generative artificial intelligence (AI) technologies to support student learning outcomes, for example, by offering personalized experiences, 24 h conversational assistance, text editing and help with problem-solving. We review contemporary perspectives on the value of AI as a tool in an educational context and describe our recent research with undergraduate students, discussing why and how we integrated OpenAI tools ChatGPT and Dall-E into the curriculum during the 2022–2023 academic year. A small cohort of games programming students in the School of Computing and Digital Media at London Metropolitan University was given a research and development assignment that explicitly required them to engage with OpenAI. They were tasked with evaluating OpenAI tools in the context of game development, demonstrating a working solution and reporting on their findings. We present five case studies that showcase some of the outputs from the students and we discuss their work. This mode of assessment was both productive and popular, mapping to students’ interests and helping to refine their skills in programming, problem-solving, critical reflection and exploratory design.

The reconstruction and representation of ancient artifacts and scenes through illustration is a cornerstone in the communication of archaeological findings. Sketches of the past have transformed over time, incorporating broader technological changes, from photography to the digital tools that have become prevalent through the twenty-first century. Most recently, developments in generative artificial intelligence (AI) promise to reshape the way we represent the past to professional and public audiences. This article shows how to use an accessible and inexpensive artificial intelligence platform to generate complex archaeological illustrations. As a case study, we create multiple scenes representing competing hypotheses about Neanderthal behavior. Using the images to visually communicate alternative hypotheses, we demonstrate how archaeological illustration using artificial intelligence promises to democratize the production of visual representations of the past. La reconstrucción y representación de artefactos antiguos y escenas a través de la ilustración es un pilar fundamental en la comunicación de hallazgos arqueológicos. Las ilustraciones del pasado han evolucionado con el tiempo, incorporando cambios tecnológicos más amplios, desde la fotografía hasta las herramientas digitales que se han vuelto predominantes a lo largo del siglo XXI. Más recientemente, los avances en la inteligencia artificial generativa prometen reconfigurar la forma en que representamos el pasado ante audiencias profesionales y públicas. En este artículo se muestra cómo utilizar una plataforma de inteligencia artificial accesible y económica para generar ilustraciones arqueológicas complejas. A través de un estudio de caso, creamos múltiples escenas que representan hipótesis competidoras sobre el comportamiento neandertal. Utilizado para comunicar visualmente hipótesis alternativas, demostramos cómo la ilustración arqueológica utilizando inteligencia artificial promete democratizar las representaciones visuales del pasado.

This article explores the question as to whether images generated by Artificial Intelligence such as DALL-E 2 can be considered artworks. After providing a brief primer on how technologies such as DALL-E 2 work in principle, I give an overview of three contemporary accounts of art and then show that there is at least one case where an AI-generated image meets the criteria for art membership under all three accounts. I suggest that our collective hesitancy to call AI-generated images art stems from the lack of a clear author figure. I propose two possible complementary solutions. First, that some AI-generated images as artworks are conjunctively authored by both the developers of the AI and the prompt-giver. Second, that AI image generators can themselves be considered works of art authored by the developers. I conclude by way of suggesting that we might have separate art competitions specifically for AI-generated artworks.