Bu çalışmada, çevrimiçi uzaktan eğitim gören öğrenciler açısından memnuniyetlerine katkıları topluluk hissi ve teknoloji kabul ölçeği kullanılarak araştırılmıştır. Çalışmada, ilişkisel ve betimsel araştırma modeli kullanılmıştır. Kullanılan ölçeklerin uygulandığı araştırma grubu, KKTC’de bulunan Uluslararası Kıbrıs Üniversitesinde 2020–2021 güz yarıyılında uzaktan öğrenim gören 406 üniversite öğrencisi oluşturmuştur. Veri analizi aşamasında kanonik korelasyon analizi, çoklu regresyon analizi, çoklu aşamalı regresyon analizi ve betimsel istatistikler kullanılmıştır. Verilerin analizi sırasında SPSS 24 yazılımı kullanılmıştır. Elde edilen neticelere göre; öğrenci memnuniyeti ile topluluk hissi ve teknoloji kabulü arasında pozitif ve yüksek seviyede ilişki olduğu belirlenmiştir. Çevrimiçi öğrenme sistemlerinde duyuşsal değişkenin öğrencilerin, çevrimiçi uzaktan eğitim-öğretime yönelik memnuniyetlerinin öğrenmeleri açısından yararlı olduğu ortaya çıkmıştır. Duyuşsal, eylemsel ve algılanan kolaylık değişkenlerinin üniversite öğrencilerinin uzaktan eğitime yönelik memnuniyetlerine anlamlı ve pozitif bir etkisi olduğu saptanırken, algılanan yarar değişkeninin anlamlı fakat negatif bir etkisi saptanmıştır. Bu sonuca göre üniversite öğrencilerinin yüz yüze eğitimi daha yararlı gördükleri anlaşılmaktadır. Çevrimiçi öğrenme sistemlerinde duyuşsal değişken ve algılanan yarar boyutları, topluluk hissi ve kabul ölçeği boyutlarının memnuniyet boyutları ile ilişkisi incelendiğinde en yüksek değerleri veren değişkenler olduğu görülmüştür.

This study aims to conduct a descriptive analysis and evaluation of graduate theses regarding decision support systems carried out between 1989 and 2020 in Turkey. The qualitative research methodology was applied, the theses were analyzed through the descriptive content analysis technique. Forty-eight graduate studies accessible from the national thesis center database of YÖK were included in the analysis. The theses were coded according to the date of publication, university, institute, department, degree level, the academic title of the thesis supervisor, thesis language, research methodology, research sub-areas. Graduate studies on DSS have increased in the last 15 years, and 42 studies have been conducted in the last 15 years. Selçuk University ranks first with the most studies on DSS. Half of the studies were carried out in the Institute of Science, and most of the studies that were produced in the universities were master's theses. Most supervisors were "Prof. Dr." titled faculty members. Most of the theses were written in Turkish, and primarily experimental studies were conducted. The business administration department produced most of the theses on DSS. Business and environment studies were the primary disciplines that produced theses. These were carried out in 7 institutes and 26 different departments. The findings of this study will guide other researchers who are willing to work in the decision support systems field.

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.