This study investigates how artificial intelligence (AI) can be integrated into first-year design education. The goal of the study is to examine how students combine orthographic projections with AI-generated images. To answer this question, a design representation course called Visual Communication I (VC-I), taught at Istanbul Technical University’s Faculty of Architecture, was selected. A three-step assignment called “ISO-meets-AI” was planned and implemented in the VC-I. The steps of the assignment were as follows: (i) producing orthographic projections of created physical compositions; (ii) generating image output from text input using an AI program (Midjourney); and (iii) combining the AI-generated images with the orthographic projections. The assignment was completed by 50 students from the departments of architecture, interior architecture, and industrial design. Tutors defined the evaluation categories and sub-criteria based on an initial analysis. While tutors evaluated every submission based on the established categories and sub-criteria, students only assessed their own work through a survey. Lastly, a second survey was given to the students in order to better understand their willingness to employ AI in their future studies. The first survey’s comparative evaluation results of the tutors and students, as well as the results of the second survey, are presented.

Purpose: The relationship between the child and his/her physical environment is an area of interaction that includes social, psychological and cultural factors along with the spatial experience, perception and behavior of the child. This study is based on the effects of spatial perception and behavior of the child within the physical environment of primary schools. In this direction, the purpose of this paper is to investigate how spatial and physical characteristics of primary school typologies affect the spatial perception and behavior of the child. Also, the parameters affecting spatial perception and behavior are examined. Design/methodology/approach: The question to be investigated is how the spatial and physical characteristics of the school’s physical environment affect the child’s spatial perception and behavior in primary schools with different typologies. Within this scope, Istanbul’s Kagithane region is selected as a case study. Schools are chosen for their similar spatial and dimensional features and similar socio-economic environment. The methodology of the study consists of a literature review, an observational study carried out to discover the interaction between the child and his/her school building and the analysis of the student’s cognitive maps. These maps were evaluated according to topological, projective, metric and imaginative parameters. Findings: The results show spatial organization and physical characteristics of primary school buildings with a structure that allows for change and transformation, and contributes to the physical and cognitive development of children. Originality/value: This study will provide an opportunity to develop the design of future primary school buildings that can support the spatial perception and spatial experiences of the children.

This research aims to explore the potential application of AIbased techniques in the introduction of design representation during first-year education. Specifically, the study examines the integration of traditional representation techniques used in design education with AI algorithms through an assignment implemented in a course namely Visual Communication 1. This course is part of the Integrated Foundation Studio (IFC) model, which has been implemented at Istanbul Technical University, Faculty of Architecture for the past 8 years. The assignment was completed by 64 students from three different departments (architecture, interior architecture, industrial design) who were all freshmen with little prior knowledge and experience in design representation and digital tools. The assignment has three steps as: (i) creating a composition with basic solids and producing an isometric projection of the composition by hand drawing, (ii) generating a series of images by using an AI-based tool Midjourney by defining keywords, and (iii) combining the previous two productions using various techniques. As a result of this assignment, 50 out of 64 students produced and submitted their personalized and unique design representations. The outcomes of the assignment are qualitatively evaluated under two main categories as Combining Techniques and Atmospheric Elements. While the first main category Combining Techniques refers to types of used modification and visualization techniques, Atmospheric Elements deals with texture, color, and forms derived from AI-generated images. Initial findings indicate that text-to-image techniques of AI contribute to enhancing the interpretation and composition skills of freshmen while producing new visual representations by making use of source images and technical drawings.

Concrete construction is associated with high environmental impact and geometric limitations imposed by conventional formwork, which has led to growing interest in combining structural optimization with robotic fabrication. In this study, structural optimization refers to computational methods such as topology optimization, shape optimization, and form finding that aim to improve material efficiency and load-bearing performance by modifying the geometry of structural elements. This systematic review investigates how these optimization approaches are translated into fabrication-aware design workflows for robotic concrete construction. Following a PRISMA-based methodology, 90 peer-reviewed studies published between 2015 and 2025 were analyzed. The review focuses on fabrication routes including (i) 3D concrete printing, (ii) 3D-printed formwork, (iii) shotcrete-based additive manufacturing, and (iv) controlled casting systems, and examines how each route constrains geometry representation, design decisions, toolpath generation, and robotic execution. The review analyzes design-to-fabrication workflows that link optimized structural geometry to production logic and process control. Key findings indicate that incorporating fabrication constraints at early design stages can support buildability and potential material efficiency, while reinforcement integration and quality control remain critical challenges for structural reliability. The review also highlights the increasing role of in situ sensing and feedback-driven automation in improving process stability. Overall, the study clarifies current practices, limitations, and emerging directions for integrating structural optimization with robotic concrete fabrication.