Giriş ve Çalışmanın Amacı: 1765 yılında Jamess Watt’ın buharlı makineyi bulması ile Endüstri Devriminin gerçeklemiş ve teknoloji kavramı günlük hayata dâhil olmuştur. Son yıllarda teknoloji alanında ortaya çıkan hızlı gelişmeler bilgi ve iletişim teknolojilerinin de aynı hızla gelişmesini sağlamıştır. Bilgisayar sistemleri yazılım sektöründeki önemli gelişmelerle birlikte çok daha donanımlı hale gelirken, sanal gerçeklik, arttırılmış gerçeklik, meta-evren gibi kavramlar ortaya çıkmıştır. 1980’li yıllarda ilk kez konsept olarak ortaya çıkmış olan Metaverse kavramı, 2000 ve sonrasında çok hızlı gelişme göstermiştir. Çalışmanın amacı ulusal akademik yayınlarda metaverse çalışmalarının hangi temalar üzerinden çalışıldığını tespit etmek ve konu hakkındaki akademik yaklaşımı belirleyebilmektir. Kavramsal/Kuramsal Çerçeve: Uluslararası değişim sinyalleri ve senaryoları içinde bugün mevcut koşullarda mimarlığın metaverse ile ilişkisini belirleyebilmek yakın gelecekte Türkiye koşullarında olası değişim senaryolarını belirleyebilmek açısından önemlidir. Bu amaçla, mimarlık-metaverse ilişkisinin tarafsız ve güvenilir olarak ele alındığı bilimsel ortamdaki bilgi, yaklaşım, düzey ve içeriğinin irdelenmesi bu çalışmanın ana hedefi olmuştur. Ulusal akademik yayınlara ait anahtar kelimeler, yaklaşımları ve yayınların niteliği gelecekte bizleri bekleyen ulusal akademik ortama ve piyasalara ait bilgiler taşır. Bu nedenle araştırma materyali olarak ulusal akademik yayınlar kullanılmıştır. Metaverse- mimarlık ilişkisi tema ve içerikleri bakımından değerlendirilmiştir. Yöntem: Çalışmanın yöntemi, odak konusu olan metaverse yayınlarının metin içinde tekrar edilen kelimeleri, kelimelerin tekrar sıklığı, metinlerde vurgulanan temalar ve temaları işaret eden anahtar kelimeler üzerinden içerik analizi yöntemidir. Bulgular: Yakın gelecekte fiziksel dünyadan tamamen kopmadan metaverse’ün ayrılmaz bir parçamız haline geleceği, çalılmada incelenen akademik yayınlarda en çok yinelenen temaların; teknoloji, eğitim ve mimarlık olduğu, bu temaları eğitim, arttırılmış gerçeklik ve simülasyon temalarının takip ettiği gözlemlenmiştir çalışma sonunda elde edilen bulgular arasındadır. Sonuç: Tüm yaşam biçimlerimizi değiştirmesi muhtemel olan bu yeni sanal evrenin mimarlar ve mimarlık sektörü için yakın gelecekte yeni bir iş modeli haline geleceği öngörülmüştür. Araştırma sürecinde metaverse kavramının pek çok farklı tema ile farklı disiplinlerin çalışma alanında yer aldığı görülmüştür. Bu durum yakın gelecekte birçok meslek alanı için önemli değişimler olabileceğinin sinyallerini vermiştir.

Background: Virtual reality (VR) technology offers an innovative learning environment that enriches user experience in education. However, there is limited research on the impact of physical environmental components within VR-supported educational spaces on user perception and satisfaction. Objectives: This study aims to evaluate user experience and satisfaction levels in VR-supported educational environments through a mixed-methods approach, focusing on the influence of physical environmental components. Methodology/Approach: Four distinct VR platforms (Mozilla Hubs, Engage, Meta Horizon, and MeetinVR) were selected as experimental settings. Twenty participants, comprising ten interior architecture students and ten faculty members from Istanbul Gelisim University, received orientation sessions to familiarize themselves with the VR equipment and platforms. Each participant engaged in 10-minute sessions on each platform, experiencing four different virtual environments in total. Following each session, participants completed structured experience questionnaires and open-ended feedback forms addressing physical environmental elements such as material, texture, lighting, form, color, size, and scale. The collected data were analyzed using IBM SPSS and MAXQDA software. Findings and Discussion: The results demonstrate that the design elements of VR-based learning spaces have a direct effect on user perception and satisfaction. In particular, material, lighting, and scale components play a significant role in enhancing users’ sense of realism and educational effectiveness. The study offers practical recommendations for the user-centered design of VR-supported educational environments, contributing to the advancement of virtual learning spaces within interior architecture education.

This study presents a quantitative metric-based comparison of four state-of-the-art text-to-image generative AI systems for architectural visualization: OpenAI Image Generation, Imagen 4, Midjourney v8.1, and Stable Image Ultra. The models were evaluated using the Architectural Prompt Dataset (APD-50), a controlled, expert-informed prompt set consisting of 50 architectural prompts across five typologies: Modern Residential Buildings, Public Buildings, Religious and Cultural Architecture, Interior Design, and Facade and Sustainable Architecture. For each prompt, four images were generated per model–prompt combination, resulting in a corpus of 800 images. The generated images were analyzed using reference-free and image-level computer vision metrics, including CLIP Score for prompt alignment, Raw BRISQUE and Quality_BRISQUE for reference-free perceptual quality, Shannon Entropy as a diagnostic descriptor of visual information density, LPIPS as intra-prompt perceptual variation, and SSIM as intra-prompt structural similarity. In response to the limitations of treating entropy as an inherently positive indicator, Shannon Entropy was excluded from the revised composite score and retained only as a diagnostic metric. Alongside the individual metric results, this study proposes the Revised Architectural AI Score (AIS-R) as a first-stage, exploratory composite index for summarizing selected visual-output characteristics under controlled prompt conditions. AIS-R combines normalized CLIP, Quality_BRISQUE, and LPIPS components using an explicit and reproducible weighting structure. The purpose of AIS-R is not to replace professional architectural judgment or to establish a fully validated architectural-quality framework at this stage. Rather, it provides an initial operational formulation for comparing prompt alignment, reference-free perceptual quality, and intra-prompt perceptual variation within the limited scope of the APD-50 experimental setup. Linear mixed-effects models with prompt-level random intercepts were used as the primary inferential framework. Under the revised metric configuration, Midjourney v8.1 achieved the highest AIS-R score (0.6504 ± 0.0586), followed by Stable Image Ultra (0.6267 ± 0.0582), Imagen 4 (0.6069 ± 0.0707), and OpenAI Image Generation (0.5829 ± 0.0555). However, these differences should be interpreted as metric-based differences in selected visual-output characteristics rather than as evidence of architectural quality, constructability, spatial validity, scale accuracy, or professional workflow suitability. The study therefore represents an initial metric-formulation and controlled benchmarking stage, while future research should examine the correspondence between AIS-R and professional architectural judgments through expert evaluation, user studies, or workflow-based validation.

This study aims to position the use of artificial intelligence in architectural design, not merely as a tool for visual generation, but as an exploratory decision-support framework in which selected design relationships can be represented in an interpretable and transparent manner. To this end, the study developed ArchAI 2.0, a five-stage hierarchical system that models semantic and spatial relationships between two-dimensional floor plans and generated building facades. The objective was to conditionally generate exterior building image alternatives using floor plan-derived structural information. Following a pair-integrity screening, 657 valid floor plan–exterior photograph project pairs sourced from the ArchDaily platform were retained and partitioned at the project level into training (n = 459), validation (n = 99), and independent test (n = 99) sets. Plan-derived geometric information was extracted using a deterministic computer-vision parsing pipeline and subsequently processed into structural tokens and a depth-aware parametric facade skeleton. The resulting skeleton representation was used as conditioning guidance for a ControlNet integrated with Stable Diffusion 1.5. A Plan-Edge ControlNet trained and evaluated under the same protocol was used as the primary conditioned baseline, while an unconditional text-only Stable Diffusion 1.5 model provided a secondary reference. On the independent test set, the Skeleton and Plan-Edge models showed comparable performance, with no statistically significant differences in Chamfer Distance, Edge IoU, SSIM, or CLIP cosine similarity after Holm correction. Relative to the text-only baseline, Skeleton conditioning produced a modest but statistically significant improvement in Edge IoU (0.0611 vs. 0.0574; Holm-adjusted p = 0.031), whereas the text-only model achieved significantly higher CLIP similarity (0.2749 vs. 0.2647; Holm-adjusted p < 0.001). FID was treated as a descriptive dataset-level measure because of the limited test set size. A bounded human conditioning adherence evaluation further showed that 58 of 98 evaluable cases reproduced the requested two-story organization (59.18%; 58.59% across the full 99-case test set), with a mean absolute story error of 0.4184 stories. Opening-column correspondence was more limited: across 87 evaluable cases, bipartite matching at the primary threshold of τ = 0.08 normalized facade width yielded a micro F1-score of 0.2970. An independent second-rater assessment on a predefined 25-case subset demonstrated high human annotation reliability, with 96.00% exact agreement and linearly weighted Cohen’s κw = 0.9351 for story-count ratings and an inter-rater opening-column matching F1-score of 0.7692. Rather than demonstrating uniform superiority or guaranteed realization of user-specified constraints, the findings indicate that the proposed depth-aware representation provides an alternative structured conditioning mechanism with a performance comparable to direct plan-edge guidance, while human evaluation reveals a clear distinction between representational editability and actual conditioning adherence. The deterministic rule engine complements this generative pipeline as an interpretable rule-based decision-support layer by expressing selected architectural relationships through explicit and reusable rules.

Background: The transition from physical classrooms to virtual environments requiresrethinking learning spaces in both spatial and pedagogical terms. This shift involves not onlya change in setting but also a reconfiguration of learning experiences and habits. Despiteshared goals of effective learning, physical and virtual classrooms differ significantly inqualitative aspects. The absence of legal regulations and the dominance of non-architectprofessionals in designing virtual environments highlight a major gap in achievingstandardized and high-quality educational spaces.Objectives: This study aims to develop a comprehensive design guide for virtual learningenvironments within online virtual reality (VR) platforms, with the objectives of enhancinge-learning quality, fostering interactive educational experiences, and establishing criteria forhow virtual environments should be designed to support effective learning.Methodology: Four virtual reality (VR) platforms—Mozilla Hubs, Engage, Meta Horizon,and MeetinVR—were selected as experimental settings. On each platform, participantsattended as listeners a 30 -minute theoretical lecture on architecture, material use, and theIndustrial Revolution, ensuring consistent educational content across all environments. Thestudy involved 20 participants—10 interior architecture students and 10 faculty membersfrom the Istanbul Gelişim University Interior Architecture Department. A two-hourorientation session was conducted to familiarize participants with the VR headsets andcontrollers before the experiment. Afterwards, each participant completed a total of 120minutes of immersive virtual education. Following the sessions, participants filled outstructured questionnaires and open-ended feedback forms assessing material, texture,lighting, form, color, size, and scale. The collected data were analyzed using IBM SPSS andMAXQDA to evaluate spatial perception and user experience.Findings: Results show that design elements of VR-based learning spaces directly affectuser perception and satisfaction. Material, lighting, and scale notably enhance realism andeducational effectiveness. The study provides practical recommendations for user-centereddesign in VR-supported learning environments, contributing to the development of virtualeducational spaces in interior architecture education.

Throughout human history, architecture has moved beyond being merely a means of meeting the need for shelter; it has evolved into a dynamic discipline standing at the intersection of aesthetics, technology, social values, and spatial politics. However, the global ecological crises, rapid urbanization, technological acceleration, and socio-economic transformations brought by the 21st century compel us to radically rethink both educational practices in academia and implementation methods in the field. This transformation is creating new connections between architectural theory, pedagogy, urban design, building physics, and emerging building simulation technologies. Architectural Practice, Theory, and Education: Technical Details and Spatial Solutions brings together studies that reflect this multidisciplinary evolution. The chapters explore radical pedagogy and crisis-driven urban studios, user-centered neighborhood parks developed through university-local government collaboration, the fragmented continuity and spatial legibility of abandoned industrial heritage through the olive oil factories of Edremit Bay, socio-technical frameworks analyzing the building performance gap, and Finite Element Method (FEM) alongside artificial intelligence-based sound insulation predictions in building acoustics. Despite their different scales and application areas, these studies share a common direction: building strong bridges between theoretical discourse and practical space, as well as macro-urban decisions and micro-technical details. From the institutional limits of radical pedagogy to the design of urban green infrastructure at the neighborhood scale, from the interpretation and continued legibility of abandoned industrial structures to diagnosing socio-technical discrepancies in building performance, and from these discrepancies to multi-scale acoustic detailing solutions, this volume highlights how holistic architectural approaches can address both present and future urban and structural challenges. I believe that the real value of architectural design and intelligent building systems lies not only in their ability to produce physical results, but also in their capacity to make environmental uncertainty manageable, spaces more equitable, and technical decisions more trustworthy for human experience. In this respect, the studies presented in this volume represent different perspectives on a common scientific objective: building more capable, interpretable, and resilient spatial systems that truly interact with humans and the environment. I hope this book will contribute to researchers, academics, students, and professionals working across architecture, urban planning, and building sciences, while also inspiring new interdisciplinary studies between theory and practice. I would like to thank all contributing authors, reviewers, and supporting institutions for their valuable efforts in bringing this collective volume to completion.