A narrative inquiry framework for AI-supported reflective thinking in architectural design education

Purpose As architectural design education faces profound technological shifts, the integration of generative artificial intelligence (AI) presents unprecedented opportunities for conceptual exploration, yet its synergy with human-centered, meaning-making methodologies requires further investigation. This study examines how generative AI can be integrated with narrative inquiry to enhance creativity and concept development in architectural design education. Design/methodology/approach Implemented in a second-year design studio, the research used a four-stage framework involving artwork interpretation, conceptual visualization, AI prompt development and iterative nonlinear design supported by stage-specific self-evaluation. This structure connects artistic interpretation with conceptual design, enabling students to work through reflective, feedback-driven processes that encourage ongoing refinement of ideas. Findings The combination of narrative thinking and AI-generated imagery improved text-to-image translation, expanded exploratory brainstorming and strengthened adaptive problem-solving. The interaction between students’ narrative concepts and AI outputs fostered co-evolutionary creativity, allowing for deeper conceptual exploration, enhanced spatial reasoning and sustained reflective practice. Originality/value The findings highlight the value of a cyclical, human-centered design pedagogy that contrasts with linear approaches to AI integration. By embedding AI within narrative inquiry, the study shows how students can maintain design agency while leveraging AI-supported visualization and ideation. Overall, the research provides insight into how narrative-driven AI methods can cultivate critical thinking, creative iteration and more reflective learning in architectural design education.