Yapay Zekâ Destekli Kişiselleştirilmiş Öğrenme Deneyimleri Bağlaminda Mimarlik Pedagojisinin Yeniden Çerçevelenmesi

Öz

Bu çalışma, yapay zekâ (YZ) destekli araçların mimarlık eğitiminde kişiselleştirilmiş öğrenmeyi nasıl destekleyebileceğini entegratif bir literatür sentezi aracılığıyla incelemektedir. Çalışma, YZ’yi yalnızca teknik bir araç olarak değil; geri bildirim süreçlerini, tasarım yargısını, yazarlık anlayışını ve stüdyo kültürünü dönüştüren bir pedagojik aktör olarak ele almaktadır. Mimarlık eğitiminin stüdyo merkezli ve disiplinler arası yapısı, öğrenen çeşitliliğini tek tip müfredat ve sınırlı geri bildirim mekanizmalarıyla her zaman karşılayamamaktadır. Eğitimde YZ destekli kişiselleştirme, adaptif öğrenme, tasarım eğitimi ve mimarlık stüdyosu pedagojisine ilişkin güncel çalışmalar temel alınarak öğrenen ihtiyaçlarını pedagojik güvencelerle ilişkilendiren kavramsal bir çerçeve geliştirilmiştir. Bu çerçeve; üretken yapay zekâ, analitik-tektonik araçlar, görselleştirme ve sunum araçları ile öğrenme rehberliği sistemlerinden oluşan dört işlevsel küme etrafında yapılandırılmıştır. 

Anahtar Kelimeler

• Mimarlık eğitimi
• eğitimde yapay zeka
• mimarlık pedagojisi
• kişiselleştirilmiş öğrenme
• öğrenci merkezli öğrenme
• geri bildirim sistemleri
• öğrenme deneyimi

Reframing Architectural Pedagogy Within AI Supported Personalized Learning Experiences

Öz

This study examines how artificial intelligence (AI)-supported tools can contribute to personalized learning in architectural education through an integrative literature synthesis. Rather than treating AI as a purely technical layer, the study positions it as a pedagogical actor that reshapes feedback practices, design judgment, authorship, and studio culture. The studio-centered and interdisciplinary nature of architectural education often struggles to address learner diversity through uniform curricula and limited feedback systems. Drawing on recent studies of AI-supported personalization, adaptive learning, design education, and architectural studio pedagogy, the article develops a conceptual framework that links learner needs with pedagogical safeguards. The framework organizes AI-supported practices into four functional clusters: generative AI, analytical-tectonic tools, visualization and presentation tools, and learning-guidance systems. 

Anahtar Kelimeler

• Architectural education
• AI in education
• architectural pedagogy
• personalized learning
• student-centered learning
• feedback systems
• Learning Experience

Kaynakça

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