Reimagining Tradition: A Comparative Study of Artificial Intelligence and Virtual Reality in Sustainable Architecture Education
Ying Cao, Xuewen Gao, Hanfang Yin, Kexin Yu, Di Zhou
Abstract
Artificial intelligence and virtual reality technologies have significant potential in traditional architectural education. Historically used separately, their educational impacts are not fully understood. To advance sustainable architecture education, this study incorporates language and illustration tools of artificial intelligence, along with immersive painting and simulation capabilities of virtual reality, into the curriculum of Jiangnan traditional architecture. Through a randomized controlled trial, 60 students were divided into AI, VR, and control groups. Based on the establishment of an adaptive course learning assessment system, this study empirically compares the effects of artificial intelligence methods, virtual reality methods, and traditional teaching methods across four dimensions: architectural knowledge, architectural design, architectural computation, and architectural learning. Independent sample t-tests and one-way analysis of variance are used to validate the differences in the effectiveness of these technological applications. Findings reveal that artificial intelligence notably enhances design and learning outcomes, whereas virtual reality shows pronounced effects in bolstering knowledge acquisition and computational tasks. Artificial intelligence proves particularly suited to conceptualization and narrative-based design tasks, while virtual reality aligns closely with model creation and post-design refinement activities. These findings provide new perspectives for hybridizing artificial intelligence and virtual reality in sustainable architecture education, contributing to sustainable outcomes.