🤖 AI Summary
This study investigates how locally deployed, domain-specific generative AI tools can effectively enhance architectural students’ creativity, foster inclusive learning, and develop their AI literacy during design tasks. Innovatively embedding generative AI deeply into architectural education, the research employs a mixed-methods approach with focus group designs to empirically examine the synergistic mechanisms among constructivist, connectivist, and Universal Design for Learning theories within AI-augmented pedagogy. Findings demonstrate that this integrated approach significantly improves students’ creative fluency, increases engagement among learners from diverse backgrounds, and strengthens their confidence in AI-assisted design. The work thus offers both a theoretical foundation and a practical framework for the meaningful integration of AI in architectural education.
📝 Abstract
The "Gen-AI-tecture" project embeds a locally executed, discipline-specific tool into a mixed-methods focus-group design, structured around three research objectives: (a) to evaluate how generative AI tools impact students' creativity in design-thinking processes and outcomes, (b) to assess whether these tools enhance inclusivity in learning processes, and (c) to examine how they develop students' AI-handling skills with a view to boosting future employability. Findings indicate enhanced creative fluency, broadened participation across diverse learner profiles, and strengthened confidence in AI-supported design processes. The study contributes evidence-based guidance for integrating generative-AI workflows into architectural pedagogy, demonstrating how such tools can operationalise constructivist principles of learner-led meaning-making, support connectivist understandings of learning as participation in human-AI networks, and advance universal learning theories by promoting more inclusive, flexible and accessible educational practices for contemporary learners.