Tasarım Stüdyosu Pedagojisinde Yapay Zeka Uygulamalarının Sistematik Analizi

Abstract

Bu çalışma, yapay zekanın (YZ) tasarım stüdyo pedagojisine entegrasyonu üzerine yapılan son araştırmaların sistematik bir incelemesini yürüterek tasarım süreçleri, öğrenme sonuçları ve öğretmen-öğrenci etkileşimi üzerindeki etkilerini belirlemeyi amaçlamaktadır. Nitel araştırma metoduna dayanan çalışmanın verileri belirlenen anahtar kelimelerin Google scholar, Scopus ve Web of Science veri tabanlarında aranması ile elde edilmiştir. Ulaşılan çalışmalar dahil etme kriterlerine göre elenerek çalışmanın veri setini oluşturan 21 makale üzerinden araştırma şekillenmiştir. Bulgular, yapay zekânın stüdyo ortamında fikir üretme partneri olarak rolü, öğrenme çıktıları ve performans göstergeleri etkileri yönüyle tasarım süreci değişimi, yaratıcılık gelişimi ve kavramsallaştırma becerisi artışı ve tasarım süreci aşamalarından erken kavramsal aşamanın desteklenmesi yönüyle üç temel alan üzerinde katkıda bulunduğunu göstermektedir. Bu etkilerin yanı sıra, yapay zekanın stüdyo ortamlarında eğitmenleri desteklemedeki rolü ve eğitmen-öğrenci ilişkisini ne şekilde etkileyebileceğine yönelik veriler eksik kalmıştır. Sonuçlar ayrıca, yapay zeka araçlarının hızlı benimsenmesine rağmen, mevcut araştırmaların genel olarak küçük ölçekli vaka çalışması ya da deneysel atölye uygulamalarından oluşması sebebiyle kolektif pedagojik sonuçların parçalı ya da yetersiz kaldığını vurgulamaktadır. Bu nedenle, yapay zekanın tasarım iş akışlarını, bilişsel süreçleri, öğrenci performansını, eğitmen-öğrenci ilişkisini nasıl etkilediğine dair daha sistematik ve kapsamlı değerlendirmelere ihtiyaç vardır. Aynı zamanda, belirli tasarım aşamalarından ziyade uzun vadeli dahil etme ile tüm süreç aşamalarına olası katkılarının ve entegrasyonunun da sorgulanması gerekmektedir. Çalışma kapsamında ortaya konulan veriler, tasarım stüdyosu ortamlarında yapay zekâ araçlarından yararlanmaya yönelik sistematik bir analiz sunması bakımından önem taşımaktadır. Elde edilen bulguların, yapay zekâ araçlarının tasarım stüdyosu pedagojisine entegrasyonuna ilişkin bir çerçeve oluşturması nedeniyle alana anlamlı katkı sağlayacağı öngörülmektedir.

Keywords

• Yapay zeka,
• Tasarım stüdyosu
• Stüdyo pedagojisi
• Öğrenme performansı

A Systematic Analysis of Artificial Intelligence Applications in Design Studio Pedagogy

Abstract

This study aims to identify the effects of integrating artificial intelligence (AI) into design studio pedagogy by conducting a systematic review of recent research on the subject, with a particular focus on its impact on design processes, learning outcomes, and instructor–student interaction. To ensure a structured and replicable analysis, a systematic review methodology was adopted. Accordingly, the study was designed based on qualitative research methods, and the data were collected through keyword searches in Google Scholar, Scopus, and Web of Science using the following combinations: “artificial intelligence” AND “design studio pedagogy,” “AI in architectural education,” “AI-assisted design education,” “AI AND design education,” “studio-based learning” AND “AI,” “human-AI collaboration” AND “design studio.” After applying the inclusion criteria, 21 articles were selected to form the data set. These studies were categorized along four analytical dimensions: the role AI assumes in the studio, the scope of its influence, the design stage at which it is utilized, and the methodological approach of each study. The findings indicate that AI contributes to design studio environments across three main areas: (1) its role as an idea-generation partner; (2) its influence on learning outcomes and performance indicators, including changes in the design process, enhancement of creativity, and improved conceptualization skills; and (3) its support of the early conceptual stages within the design workflow. However, several studies note that using AI as an idea-generation partner does not always correspond to improved design quality or enhanced creativity. Similarly, while AI tools may accelerate workflow and increase time efficiency, this does not necessarily translate into higher-quality design outputs. In addition to these contributions, some studies highlight disadvantages such as maintaining design logic, integrating AI-generated outputs into coherent and conceptually consistent design ideas, and addressing ethical concerns. Furthermore, the data remain insufficient regarding AI’s role in supporting instructors within studio environments and its potential effects on instructor–student relationships. The results also emphasize that despite the rapid adoption of AI tools, current research largely consists of small-scale case studies or experimental workshop implementations, leading to fragmented and limited collective pedagogical insights. Therefore, more systematic and comprehensive evaluations are needed to better understand how AI influences design workflows, cognitive processes, student performance, and instructor–student dynamics. It is additionally necessary to investigate the long-term integration of AI across all stages of the design process rather than focusing solely on specific phases. The data presented in this study are significant in offering a systematic analysis of the use of AI tools in design studio environments. The findings are expected to provide a meaningful contribution to the field by establishing a framework for the integration of AI tools into design studio pedagogy

Keywords

• Artificial intelligence
• Design studio
• Studio Pedagogy
• Learning performance

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