近年來，隨著計算處理能力的提升以及對大型數據集的利用，語言模型如ChatGPT、Copilot、Gemini…等的應用日益普及，人工智慧在文本生成、資訊檢索以及創意構思等任務中顯示出極大的潛力。在設計領域中，ChatGPT能夠根據使用者的輸入持續提供多樣化的設計建議，從而擴展問題空間，激發創新想法。儘管如此，過多的建議可能會增加設計師處理大量資訊的負擔，導致認知負荷的增加，連帶影響設計成果。基於這一背景，本研究以雙鑽石流程為基礎，透過NASA-TLX認知負荷量表評估受測者於「發現」、「定義」、「發展」、「交付」四個設計階段所感知的負荷程度，並搭配半結構化訪談深入理解負荷產生的原因與使用者互動的體驗。此外，研究亦邀請六位設計專家，針對受測者的最終設計草圖進行創造力、美學與技術可行性等構面評分，以分析認知負荷與設計成果之間的關聯性。實驗共招募30位具設計背景的大四以上學歷者，分為實驗組（使用ChatGPT與Google）與對照組（僅使用Google）進行外送機器人的設計任務。
研究結果顯示，不管有無ChatGPT的導入，不同設計階段會顯著影響受測者的認知負荷。可整體而言，實驗組與對照組在認知負荷上的差異未達統計顯著水準，顯示ChatGPT並未能顯著降低受測者的認知負荷。質性資料則指出，實驗組部分受測者認為ChatGPT有助於資訊整合與解決方案生成。然若問題定義不清，則易產生冗長或偏離需求的回覆，反而提升心智需求。此外，專家評分的結果亦顯示兩組在創造力、美學、可行性與整體表現上皆無顯著差異，顯示ChatGPT對設計成果的提升效益尚不明確。綜上所述，本研究揭示了ChatGPT在設計中的角色：一方面可協助資訊統整與生成設計解方，另一方面亦可能因資訊過載或回應不精準而彌平認知負荷之差異。透過雙鑽石模型的階段分析，也發現受測者在不同階段對ChatGPT的需求與期待不同，導致認知負荷表現出階段性的差異，彌補了以往僅於任務結束後進行單點評估的研究缺口。

In recent years, with the advancement of computational power and the increasing use of large-scale datasets, language models such as ChatGPT, Copilot, and Gemini have become increasingly prevalent. Artificial intelligence has shown great potential in tasks such as text generation, information retrieval, and creative ideation. In the field of design, ChatGPT can continuously provide diverse design suggestions based on user input, thereby expanding the problem space and inspiring innovative ideas. Nevertheless, excessive suggestions may increase the burden of information processing for designers, leading to a rise in cognitive load, which can, in turn, affect design outcomes.
Against this backdrop, this study adopts the Double Diamond process as a framework and uses the NASA-TLX cognitive load scale to assess participants’ perceived load across the four design phases: Discover, Define, Develop, and Deliver. Semi-structured interviews were conducted to further explore the sources of cognitive load and participants' experiences interacting with the tools. Additionally, six design experts were invited to evaluate the final design sketches of the participants in terms of creativity, aesthetics, and technical feasibility to analyze the relationship between cognitive load and design outcomes.
A total of 30 participants with a design background at the senior undergraduate level or above were recruited and assigned to either an experimental group (using both ChatGPT and Google) or a control group (using only Google) to complete a food delivery robot design task.
The results revealed that, regardless of the use of ChatGPT, different design phases significantly affected participants’ cognitive load. Overall, no statistically significant difference in cognitive load was observed between the experimental and control groups, suggesting that ChatGPT did not significantly reduce cognitive load. However, qualitative data indicated that some participants in the experimental group found ChatGPT helpful for information integration and solution generation. On the other hand, when the problem was poorly defined, ChatGPT tended to produce verbose or off-target responses, increasing mental demand instead. Furthermore, expert evaluations showed no significant difference between the two groups in terms of creativity, aesthetics, technical feasibility, or overall performance, indicating that the effectiveness of ChatGPT in enhancing design outcomes remains inconclusive.
In summary, this study sheds light on the dual role of ChatGPT in design: while it can assist in organizing information and generating solutions, it may also contribute to information overload or imprecise responses, thereby offsetting any reduction in cognitive load. Through phase-specific analysis within the Double Diamond model, the study also found that participants’ needs and expectations for ChatGPT varied across different design stages, leading to stage-based differences in cognitive load. This addresses a gap in previous research, which often relied solely on post-task evaluations.

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