Text-to-image GenAI integration in architecture pedagogy: skills, knowledge, and competencies: a case study

This paper investigates the impact of text-to-image generative artificial intelligence (GenAI) on architectural education from the student perspective in a case study. The study specifically focuses on AI image-generation practices and examines how students perceive these tools as influencing the expected knowledge, skills, and competencies of an architecture curriculum. A structured survey was used to evaluate the nine official programme learning outcomes (PLOs) via 19 items. Analyses include descriptive statistics and Principal Component Analysis (PCA). The results indicate that students generally perceive text-to-image GenAI as having a moderately positive influence on their learning processes, with some indicative variation across year levels. Students report the most notable perceived benefits in relation to design process and communication. PCA identified three emergent domains: (1) disciplinary foundations and professional responsibility, (2) design thinking and representational–analytical skills, and (3) integrative and applied competencies. Together, the three factors capture different ways in which AI image-generation tools are perceived within the curriculum. Based on these outcomes, it is also argued that the advent of GenAI in architecture pedagogy is not as groundbreaking as the literature often indicates. Thus, areas of concern have been highlighted, including potential overreliance, diminished independent creativity, and ethical issues related to authorship, authenticity, and professional accountability. The study offers evidence-based insight into how text-to-image GenAI is currently viewed within architectural education.