To enhance the application capabilities of large language models (LLMs) in conceptual design, this study explores how to achieve deep integration between LLM-based agents and concept generation methods using the chain-of-thought (CoT) technique and evaluates its feasibility. Using GPT-4 as a case study, we designed two agents: IntelliStorm (based on the unstructured brainstorming method) and EvoluTRIZ (based on the structured TRIZ method). Thirty participants were recruited, and through two experimental phases spaced one month apart, a comparative analysis of the effects of collaboration groups (human-agent vs. human-human) and concept generation methods (brainstorming vs. TRIZ) on participants’ physiological activation and creative thinking performance were conducted. The results show that the involvement of LLM-based agents can effectively reduce participants’ electrodermal activity(EDA )response levels, indicating a reduction in cognitive load. Moreover, these agents preserve the distinct physiological response patterns and performance advantages of the different concept generation methods. For example, IntelliStorm, like brainstorming, evokes stronger responses to information stimuli, demonstrating superior thinking fluency; EvoluTRIZ, like the TRIZ, exhibits a higher frequency of information responses, showcasing enhanced thinking elaboration. However, originality tends to favor human–human collaboration. The findings confirm that integrating LLMs with traditional concept generation methods is an effective strategy made possible by combining CoT and retrieval-augmented generation (RAG) technologies. In the future, LLM-based agents are expected to achieve broader application in the design field by incorporating additional concept generation methods.

The aim of this study is to explore how large language models (LLMs) integrated with structured versus unstructured concept generation techniques (CGTs) influence designers’ creative thinking processes and outputs. Using human–human collaboration (HHC) as a baseline, a 2 × 2 mixed factorial design was adopted to investigate the effects of collaborator type (between-subjects: LLM-based agents vs. experienced designers) and CGT type (within-subjects: brainstorming vs. TRIZ). Two LLM-based agents, IntelliStorm and EvoluTRIZ, were developed for the study, with 32 participants randomly assigned to either the HHC or human–agent collaboration (HAC) groups. Brain activity was measured using functional near-infrared spectroscopy, while outputs were assessed through expert evaluations. Results showed that designers exhibited lower cognitive load, better cognitive resource coordination, and enhanced fluency and flexibility in thinking in HAC than in HHC. Moreover, distinct patterns were revealed in different CGTs: brainstorming activated the right dorsolateral prefrontal cortex (PFC) as the core connectivity region, enhancing ideational fluency, whereas TRIZ activated the left dorsolateral PFC, facilitating refined thinking. Although HAC demonstrated stronger overall performance, HHC retained unique advantages in originality. This research offers novel neuroscientific insights and provides evidence-based guidance for developing more effective LLM-based design agents.

Digital transmission of traditional cultural heritage faces challenges of insufficient user engagement and superficial dissemination. Using the Cizhou kiln as a case study, this research proposes the “Creation-as-Transmission” design paradigm, which transcends traditional unidirectional display models by transforming users from cultural consumers to cultural co-creators. This paradigm systematically integrates situated cognition theory and co-creation theory, achieving deep cultural learning and socialized transmission through AI collaborative creation. Empirical studies ( N = 200) demonstrate that compared to traditional methods, this paradigm significantly enhances cultural cognition (74% improvement) and the proportion of spontaneous sharing on social media (82.6% sharing rate). More importantly, this research provides the first quantification of causal mechanisms between creative behaviors and cultural cognition, establishing a predictive model explaining 73.1% of cognitive variance and offering mechanistic evidence for situated learning theory’s application in cultural heritage transmission. The research contributions have threefold value: theoretically, it expands HCI understanding of the relationship between “design behaviors and cognitive effects”; methodologically, it provides a transferable implementation framework for other intangible cultural heritage; practically, it opens new pathways for digital technologies to promote sustainable cultural transmission.