An Large Language Model-Augmented Method to Assist Novice Designers in Divergent Thinking

Abstract

Divergent thinking is crucial for novice designers’ creativity and innovative problem-solving skills. Due to their limited knowledge base, novice designers struggle to master divergent thinking methods and connect personal knowledge with design experience. This study introduces a novel method that uses large language models (LLMs) to assist novice designers in expanding their divergent thinking. This method incorporates a two-layer hierarchical structure that links each of the four perspectives—memory, operation, scene, and property—to specific dimensions, facilitating the systematic generation of new divergent ideas. By combining LLMs’ vast knowledge with these divergent thinking frameworks, this method not only provides structured guidance but also fosters the gradual enhancement of divergence through interactive questioning and content generation. A series of tests, including the alternative uses test (AUT) and conceptual design tasks, were conducted to evaluate the impact of this method. Results show that LLMs may significantly improve the fluency, flexibility, and originality of divergent thinking outcomes. This study explores the potential for human–computer collaboration to support divergent thinking, opening avenues for future research and practical applications in design education and practice.