The advent of artificial intelligence (AI), in combination with the ubiquity of digital sensors, computer networks, and automation that has characterised the last decade, is transforming the socio-economic environment and defining a probable new industrial era. This evolution also inevitably involves the world of Design by redefining the designer’s role, the object of design, and user relationships. Many of today’s artefacts, whether an iPhone application, a car, or a building, are increasingly connected to the designer who conceived them, thanks to a continuous flow of data detailing many aspects of the user experience. This same information can be used to train AI neural networks capable of autonomously generating specific solutions without human intermediation. An artificial intelligence engine can thus anticipate users’ needs and behaviours, proposing solutions that are improved and customised according to the particular use that distinguishes each customer. This paradigm shift has important implications for the role of the designer. Through the analysis of pioneering case studies, this paper analyses the possible developments, delving into the repercussions for design theory and practice concerning the theoretical framework used today to interpret the discipline of Design.
Is a Picture Worth a Thousand Words? Comparative Evaluation of Generative AI for Drawing and Representation
The paper presents a comparative evaluation of generative AI systems for drawing and architectural representation, focusing on the operational principles, workflows, and visual outputs of text-to-image applications such as Midjourney, Stable Diffusion, and DALL-E 2. The research analyzes neural networks, latent space mechanisms, generative adversarial networks, autoregressive models, and diffusion probabilistic models to explain how AI systems transform textual prompts into visual representations. Through experimental prompt engineering and comparative image generation tests, the study investigates the relationship between AI-assisted creativity, visual storytelling, representation processes, and design workflows. The paper critically discusses the implications of generative AI for architecture and visual culture, including authorship, ethics, copyright, bias, realism, and the transformation of creative practices. The research ultimately proposes an informed and critical approach to AI-assisted representation, emphasizing the evolving role of designers as curators and strategic decision-makers within AI-driven creative environments.
Artificial Intelligence and Virtual Reality in the Simulation of Human Behavior During Evacuations
This paper investigates the integration of artificial intelligence and virtual reality for the simulation of human behavior in emergency evacuation scenarios. The research focuses on the use of game engines and agent-based modeling to reproduce dynamic interactions between individuals and built environments under hazardous conditions.
The methodology combines three-dimensional architectural modeling, real-time simulation environments, and AI-driven behavioral models. As described in the workflow (pp. 755–756), architectural spaces are modeled and imported into Unreal Engine, where virtual agents are endowed with physical and behavioral properties. Agent movement is governed by social force models, allowing the simulation of individual decision-making processes influenced by environmental stimuli, obstacles, and crowd dynamics. The system supports real-time interaction and visualization, enabling the testing of evacuation scenarios under varying conditions such as fire location and spatial configuration. Results demonstrate that immersive simulation environments can support predictive analysis, training, and design evaluation, while also highlighting the limitations of current models in capturing complex human behavior and decision-making processes during emergencies.
Proposal for a Data Visualization and Assessment System to Rebalance Landscape Quality
The landscape can be considered a complex system described as a non-linear entity, organized accord-ing to the connections between the different elements that characterize its state. The latter cannot be determined a priori but emerges from the multiple interactions between assets and relationships that are no longer found when the phenomenon is traced back to the individual components. Territorial development is closely connected to social, economic, cultural and symbolic issues that determine the transformative practices of space and the territorial palimpsest. If not carefully managed, these forces can lead to the dissolution of the landscape and environmental values that have been stratified in a specific land. This paper proposes the construction of a numerical spatial model that describes the territorial settlement patterns, based on methodologies and techniques typical of AI, to develop tools for re-balancing the man-landscape relationship.
Artificial Intelligency, Big Data and Cultural Heritage
In recent decades, the cultural heritage sector has benefited from solutions offered by ICT for the conservation, management, enhancement and communication of cultural heritage; today this specific sector benefits from the infinite potential of application of AI. The proposed research identifies the three main lines of research that operate in the cultural heritage exploiting the synergy between machine learning, big data and AI, starting from the analysis of the state of the art and a subsequent first taxonomic approximation of artificial intelligence systems. The analysis of some case studies developed in the field of recovery and restoration of cultural heritage, monitoring and prevention of damage, data acquisition and analysis of the same, confirm the real potential of AI: trigger knowledge from knowledge.