Generative AI-Driven Innovation in Environmental Design Pedagogy: A Framework for Human-Machine Collaborative Curriculum

Abstract

The rapid advancement of generative artificial intelligence (Generative AI) is significantly transforming design education regarding instructional content, methodologies, and assessment frameworks. The standard linear curriculum structure, focused on skill transmission, is inadequate for meeting the demands of cultivating diverse design abilities in the digital age. Grounded in the Design-Based Research (DBR) approach, this study proposes and constructs a curricular system for human–machine collaborative environmental design in the Chinese context. This system establishes a novel paradigm of student-centered, technology-enabled, and system-closed course design, founded on the "human-machine-environment trinity collaboration model." It integrates the advantages of generative AI technology with the pedagogical expertise of instructors to redesign course modules, learning activities, and feedback mechanisms. The study presents a systematic methodology for curriculum innovation in environmental design education amid digital transformation by proposing a three-stage curriculum framework of "foundation-core-expansion," introducing a four-stage spiral model of "prompt-generation-critique-reconstruction" for teaching activities, and establishing a data-driven learning assessment mechanism. This study has theoretical and practical implications for enhancing the intelligent advancement and quality assurance of environmental design education in China, addressing the strategic requirements of modern university professional education to transition towards intelligence and transparency.