隨著生成式人工智慧的快速發展，產品設計領域迎來了結合大型語言模型 (Large Language Models, LLMs)輔助構想生成的新契機 (Gordijn & Have, 2023)。其中，由OpenAI開發的ChatGPT在自然語言理解與生成方面展現出卓越能力，特別適合作為創意觸發與構想擴展的工具 (Füller et al., 2022)。然而，目前針對ChatGPT於產品設計早期構想階段之應用仍缺乏系統性實證研究。
本研究旨在探討結合傳統創意思考方法與 ChatGPT 輔助之構想生成流程，並引入提示模板工程 (Prompt Engineering)以優化生成品質。研究設計任務聚焦於實務導向的產品創新議題，藉由不同創意發想策略進行比較。參與者依構想生成方式分為三組：傳統方法組 (Group1)、傳統方法結合ChatGPT 組 (Group2)、以及傳統方法結合 ChatGPT 並搭配優化提示詞組 (Group3)，透過標準化任務及問卷，收集各組構想數量與品質 (有用性、新穎性、多樣性)進行評估。
實驗結果顯示，結合ChatGPT的組別在構想數量與多樣性上明顯優於僅使用傳統方法的組別；且在適當Prompt引導下，構想的有用性與新穎性亦有顯著提升。其中Group3於多數指標表現最佳，展現出經優化提示引導後，能兼顧數量與品質的優勢。相對地，僅單純使用ChatGPT者雖能提升構想數量，但對有用性及新穎性的促進作用有限。此外，過度聚焦提示引導亦可能略微降低構想的自然多樣性。
綜合而言，本研究驗證了生成式AI於產品構想階段的輔助價值，特別是透過提示工程優化後，對加速產品開發與強化創新能量具實質助益。未來建議進一步探索不同提示策略、跨領域應用情境及個人特質對AI輔助構想成效的影響，以深化人機協作設計模式的應用潛力。

With the rapid advancement of generative artificial intelligence and large language models, the field of product development is experiencing new opportunities for AI-assisted ideation (Füller et al., 2022). This study evaluates the impact of ChatGPT on innovative product development through an experimental design. Participants were divided into three groups: Group 1 employed only traditional ideation methods; Group 2 combined traditional methods with ChatGPT; and Group 3 integrated ChatGPT with optimized prompt engineering. All participants were tasked with completing a standardized ideation assignment, and their outputs were evaluated across four dimensions: quantity, usefulness, novelty, and variety.
The results revealed that groups incorporating ChatGPT significantly outperformed the traditional-only group in idea quantity. Among them, the group using optimized prompts (Group 3) achieved the best performance across most indicators, showing that prompt engineering effectively enhances quantity, usefulness, and novelty, while maintaining a high level of variety. However, without proper prompt guidance, using ChatGPT alone—though increasing idea quantity—had limited effects on usefulness, novelty, and diversity.
This study confirms the value of generative AI, particularly when supported by prompt engineering, in the conceptual phase of product development. Such integration accelerates the development process and enhances creative performance within design workflows. Future research could explore alternative prompting strategies, cross-disciplinary applications, and individual differences influencing the effectiveness of human–AI collaborative ideation, thereby expanding the potential of co-design between humans and AI.

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