本研究旨在探討在人智合作情境中，生成式人工智慧代理人之「回饋形式」與「回饋取向」對設計構思之影響，並進一步分析其對創造力表現與創意支持感受的作用機制。隨著生成式AI技術的快速發展，AI已逐漸從單純工具轉變為創意合作夥伴。然而，過去研究多著重於AI生成內容的品質與效率，較少深入探討人機互動過程中「回饋機制」如何影響設計者的思考歷程與創意產出。因此，本研究以回饋為核心切入點，試圖補足人智共創研究在互動層次與心理層面的不足。

本研究採用二因子組間實驗設計，以回饋形式（文字／圖像）與回饋取向（任務型／迭代型）為自變項，建構四種不同的AI回饋模式，並透過實際設計草圖的任務，觀察受試者在不同條件下的創意表現。依變項包含創造力指標（新穎性、多樣性與流暢性）以及創意支持感受。實驗系統整合生成式AI多模態能力，使AI代理人能即時根據使用者的創意成果提供文字建議或圖像草圖回饋，讓使用者進行更多的創作。使用者的創意成果則透過專家分群與生成式 AI 輔助的方法進行資料處理，以提高評量之客觀性與信度。

研究結果顯示，回饋形式與回饋取向對設計構思的影響具有構面差異與情境依賴性。在創造力表現上，圖像回饋在提升構思流暢性方面具有較穩定的促進效果，顯示視覺刺激有助於降低轉譯負荷，進而加速想法生成與延伸。相較之下，文字回饋未在新穎性上呈現整體優勢，其效果並非穩定存在，而是取決於回饋取向與任務脈絡；亦即，文字回饋在適當情境下仍可能透過語意重組促進創意突破，但在其他情況下也可能因抽象性過高而增加理解負擔。

在多樣性方面，回饋形式與回饋取向之間呈現顯著交互作用，顯示不同刺激方式對概念擴散的影響並非一致，而是受到情境條件調節：圖像回饋在迭代取向下較能透過視覺變異打破固著，文字回饋則在任務取向下較能藉由語意情境引導概念轉移。整體而言，單一回饋策略難以全面提升各項創造力指標，不同回饋模式在流暢性、新穎性與多樣性上呈現互補關係，也反映人智共創中的關鍵不僅在於生成內容本身，更在於回饋機制與互動情境的適配。

在主觀感受方面，研究發現生成式 AI 回饋不僅影響客觀創意成果，也顯著影響設計者的創意支持感受。特別是容易理解的任務型建議，能提升使用者的增能感與合作體驗，使其更願意將AI視為創意夥伴。相對而言，若回饋缺乏透明性或或無法切合設計者的設計意圖，則可能降低信任感與參與動機。此結果呼應人智合作理論中「互動品質」對創造歷程的重要性，顯示良好的回饋設計不僅是資訊提供，更是促進認知調節與創意生成的關鍵因素。

綜合而言，本研究證實生成式AI代理人在設計構思中的價值不僅來自其生成能力，更關鍵在於其回饋設計與互動方式。不同回饋形式與取向對創造力表現與使用者感受具有多層次影響，顯示人智合作應從「內容生成」轉向「互動機制設計」。本研究之貢獻在於提出系統化比較AI回饋模式的實證證據，並揭示回饋在創意歷程中的調節角色。研究結果可作為未來創意支援系統與智慧共創平台設計之參考，協助開發更具支持性、可解釋性與合作感的生成式 AI代理人合作系統。

This study investigates the effects of generative AI agents’ feedback modalities and feedback orientations on design ideation in human–AI collaboration contexts, examining their impacts on creativity performance and perceived creative support. With the rapid advancement of generative AI, AI agents have gradually evolved from merely tools into active collaborators. However, prior research has largely focused on the quality and efficiency of AI-generated outputs, with limited attention to how feedback mechanisms can influence designers’ cognitive processes and creative outcomes. This study focus on AI feedback to address gaps in interaction-level and psychological perspectives within co-creative research.

This study employs a two-factor between-subjects experimental design, manipulating feedback modality（text vs. image）and feedback orientation（task-based vs. sketch-based）, resulting in four distinct AI feedback conditions. Through a sketch-based design task, participants’ creative performance under different conditions was examined. Dependent variables include creativity metrics（novelty, diversity, and fluency）and perceived creative support. The experimental system integrates multimodal generative AI capabilities, enabling AI agents to provide real-time textual suggestions or image-based feedback based on users’ sketches. These creative outputs by users were analyzed through expert clustering with generative AI-assisted methods to enhance objectivity and reliability.

The results indicate that feedback modality and orientation have differentiated effects on design ideation. In terms of creative performance, image feedback more consistently helped improve idea fluency, suggesting that visual stimuli reduced the mental effort needed to interpret feedback and therefore helped participants generate and extend ideas more quickly. In contrast, text feedback did not show an overall advantage in novelty. Its effect was not consistent, but depended on the type of feedback approach and the task context. In other words, text feedback could still support creative breakthroughs in the right situation by encouraging semantic reorganization, but in other cases it could also become too abstract and increase the burden of understanding.

For idea diversity, there was a significant interaction between feedback format and feedback approach. This means that the influence of different types of feedback on expanding concepts was not the same across situations, but changed depending on the context. Image feedback was more helpful in sketch-based conditions because visual variation could help break fixation, while text feedback was more helpful in task-based conditions because semantic cues could guide designers toward new conceptual directions. Overall, no single feedback strategy universally improves all aspects of creativity; instead, different feedback modes exhibit complementary strengths across creativity dimensions.

In terms of the perceived creativity support, the findings show that interpretable and context-relevant feedback （e.g., task-based suggestions grounded in design context） enhances users’ sense of empowerment and collaborative experience, making them more likely to perceive AI as a creative partner. Conversely, feedback that lacks transparency or fails to align with the designer’s intent may reduce trust and engagement. These findings align with prior research, highlighting the importance of interaction quality in shaping creative processes. Effective feedback design is therefore not merely a means of information delivery, but a critical mechanism for facilitating cognitive regulation and creative generation.

In conclusion, this study demonstrates that the value of generative AI agents in design ideation lies not only in their generative capabilities but also in their feedback design and interaction strategies. Different feedback modalities and orientations have multidimensional effects on both creativity performance and user perception, suggesting that human–AI collaboration should shift from a focus on content generation to interaction mechanism design. This study contributes empirical evidence through a systematic comparison of AI feedback modes and reveals the regulatory role of feedback in the creative process. The findings provide practical implications for the design of future creativity support systems and co-creative platforms, supporting the development of generative AI agents that are more supportive, interpretable, and collaborative.

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