BIM, GenAI, and Design Knowledge: Rethinking the Design Process through AI-Assisted Façade Visualization

The advancement of Generative Artificial Intelligence (GenAI) is reshaping architectural education by altering how students visualize, reason, and construct design knowledge. While Building Information Modelling (BIM) supports structured, data-driven learning that foregrounds technical rigor, constructability, and sustainability performance, GenAI introduces image-based visualization that privileges intuition, speed, and aesthetic exploration. This shift generates pedagogical tension regarding how digital tools shape learning processes, design reasoning, and students’ professional identity within sustainable built environments. This study adopts a mixed-methods case study within undergraduate architectural design studios. Students developed building massing and façade concepts in Revit (BIM), rendered them using Veras as a BIM-integrated GenAI tool, and, when necessary, utilized alternative AI platforms. Data were collected through a post-task questionnaire that captured visual comparisons between BIM models and AI outputs, and open-end reflection questions on learning experience from the student’s point of view, which were then quantified. Findings reveal a pedagogical shift from model-based to image-based reasoning. Veras encouraged students to think through geometry consistency, performance parameters, and constructability, yet was perceived as visually constrained for creative learning. Alternative AI tools stimulated more reasonable imagination and visual storytelling. This suggests that GenAI does not merely assist visualization but actively reconfigures how students learn to integrate aesthetics, technology, and sustainability in design thinking. The paper contributes to built environment pedagogy by proposing a hybrid teaching framework that integrates BIM precision with controlled GenAI visualization. It recommends embedding critical AI literacy in studio curricula so students can critically navigate between data-driven and image-driven modes of knowing, ensuring that GenAI enriches rather than displaces professional judgment, ethical accountability, and sustainable design intent in future architectural practice. From HBIM to Environmental Intelligence: