Student perceptions of generative AI in built environment education: Evidence from a mixed-methods study and a five-pillar integration framework

This study critically examines students’ perceptions and experiences of generative artificial intelligence (GenAI) in built environment education. Employing a mixed-methods (pragmatist approach), we surveyed 277 undergraduates and postgraduates in Nigeria and Namibia, complemented by thematic analysis of open-ended responses. Quantitative findings reveal that while students value GenAI for 24/7 availability, personalised feedback, and accelerated information retrieval, they raise concerns about reliability, ethical use, and reduced human interaction. This study addresses the limited empirical evidence on how GenAI shapes learning experiences in built environment disciplines within developing educational contexts. Qualitative insights highlight strategies for optimising GenAI integration, including developing discipline-specific AI models, designing adaptive learning pathways, enacting clear implementation policies, educating learners on ethical use, and maintaining regular updates. We synthesise these into a five-pillar framework developed for enhancing learning experiences, which encompasses AI model specialization, adaptive learning pathways, ethical governance, digital literacy development, and continuous system updates, while presenting actionable guidance for curriculum designers, policymakers, and technology developers seeking to harness GenAI equitably and responsibly in built environment programmes.