Smart Integration of XR Technologies in Architectural Education: Metaverse Opportunities and Challenges
Year: 2024
Type: article
Study design: Observational Study
Abstract: Extended Reality (XR) technologies—encompassing Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR)—are transforming architectural education by offering interactive, student-centered learning within immersive Metaverse environments. This study examines the level of adoption, benefits, and challenges of XR integration in Middle Eastern architecture programs, highlighting both qualitative and quantitative insights derived from comprehensive literature reviews and a questionnaire survey of 266 participants. Results indicate that although 35% of respondents are highly familiar with XR, broader curriculum-wide integration remains fragmented. XR-based design studios are the primary area of adoption, emphasizing immersive, hands-on training that facilitates spatial understanding and sustainable design insights. Nonetheless, institutional barriers—such as limited infrastructure, substantial cost outlays, and the need for faculty training—constrain widescale implementation. Student-specific challenges include accessibility concerns, health implications of prolonged VR usage, and the scarcity of high-speed internet in some educational contexts. Despite these hurdles, XR fosters enhanced collaboration, global engagement, and real-time design prototyping, foreshadowing significant pedagogical shifts. The study recommends establishing dedicated XR labs, faculty development programs, and strong industry partnerships to support large-scale XR adoption. Future research should focus on in-depth frameworks and Participatory Action Research (PAR) to systematically integrate XR across architectural curricula. Adapting to these immersive platforms could reshape how architectural students conceive, create, and critique built environments.
Source: Smart Design Policies
Author: Hebatallah Hamdy Mohamed
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4405994802