ADAPTING ARCHITECTURAL DESIGN EDUCATION FOR THE AI ERA: PRELIMINARY FINDINGS AND FUTURE DIRECTIONS

Abstract

Architectural design courses are an essential part of many universities' curricula, offering students the opportunity to learn about building construction, building physics, mechanics, environmental ecology, and architectural aesthetics. Traditional architectural training typically starts with architectural graphics and model making by hand, which helps students understand the relationship between human scale and space scale and develops their aesthetic taste and innovative thinking. However, the rise of advanced technologies and AI products in recent years has led to a decline in students' interest in practical training. Some students prefer to use online searches to understand room size rather than taking measurements, and they would rather learn how to use 3D printers than how to make models with utility knives. This trend has prompted questions about the relevance of traditional architectural training methods to the new generation of students. Should educators abandon traditional training and adopt new technologies? This research examines traditional architectural training methods through personal teaching experience in universities, using first-stage AI skills to compare traditional methods and adjusted methods. The study seeks to determine the adaptability of traditional training methods to face AI trends while maintaining the relevance of human scale and space scale, aesthetic taste, and innovative thinking. The findings of this research offer insights into how educators can adjust their teaching methods to provide students with the necessary skills to succeed in the current and future technological environment. The study also offers discussions and possible solutions to address the challenges faced by architectural educators for future generations.