生成式人工智慧（Gen‑AI）進入設計現場後，關鍵不再只是「能生成什麼」，而是「如何激發設計創造力」。研究初期觀察顯示，Gen‑AI 的圖像生成迭代在操作與認知功能上，與建築設計中的草圖繪製（sketching）展現出若干相似性：兩者皆為支援構想外化（externalizing ideas）、記憶保持 （memory retention）、迭代探索（iterative exploration）與精煉想法（refinement）的動態平台。然而，不同於草圖媒介的手動回饋與線性發展，Gen‑AI 所引發的創意轉向往往帶有突發性與非線性邏輯。本研究聚焦於此激發創意的「觸發機制」，探討建築概念設計初期中，設計者如何在多模態（multimodality）互動中與 Gen‑AI 共創。
為回應當前 AEC（Architecture, Engineering, Construction）領域普遍偏重輸出效率、較少探討人機互動中的設計認知歷程，本研究以回溯式口語協議（retrospective verbal protocols）與設計行為編碼進行質性分析。共招募八位具建築訓練背景之研究生／初階設計者參與（2 次前導、6 次主實驗），於相同基礎影像的建築改建提案情境下，完成三種 30 分鐘任務（文字提示限定、ControlNet 草圖／影像引導限定、自主混合）。研究透過 LiblibAI 平台整合 Stable Diffusion 與 ControlNet，以蒐集螢幕錄影、影像序列與回溯敘述資料。
在分析層面，研究改寫 Suwa 等人（1998）提出之 DCOCS 認知編碼架構，增補 Wow／Good／Fail 三種設計反應作為創意觸發點（trigger points），並搭配時間軸、影像流程圖等視覺化方式進行跨個案比較。結果發現：Fail 為最常見反應，常呈現連續失敗迴圈並驅動策略修正與模態切換，形成「修正式循環」（corrective loops）；Wow 雖低頻，卻穩定標誌概念樞紐，能導致重新詮釋甚至目標重設。這類 Wow 反應可對應 Akin and Akin（1996）所稱設計歷程中的「突現性心智洞察」（Sudden Mental Insight, SMI），亦即在資訊不斷重組中突發的轉譯時刻。研究並進一步歸納出三種人機共創取向: Machine‑Led、Human‑Led、Human‑Led–Machine‑Led，顯示創意轉向並非單純仰賴工具熟練度，更受制於設計者的模態敏捷（modal agility）與對不確定性的詮釋能動性（interpretive agency）。
本研究亦延伸現有設計認知研究，從微觀互動與反應歷程中揭示創意觸發與策略轉換的關聯性。綜合而言，本研究提出一種以「觸發點（trigger）」與「策略切換」為核心的人機共創認知觀點，回應現行設計工具評估過度聚焦生成結果、而忽略設計歷程的偏向。研究亦指出，目前 Gen-AI 工具在設計節奏掌握、隨機性調整與多模態銜接上仍存在操作限制，亟需介面優化。最後，本文針對 Gen-AI 工具設計與建築教育提出具體建議，包括：建構具可解釋性的輸入—輸出對應機制、提供可調式的隨機性控制，以及促進模態間無縫轉換的工作流程，以更有效支持設計者在 AI 輔助創作中的作者性與反思迭代能力。

The emergence of Generative Artificial Intelligence (Gen-AI) in design has shifted the critical question from what can be generated to how creativity is triggered and redirected. Early observations in this study suggest that Gen-AI–driven image iteration shares several cognitive and operational parallels with sketching in architectural design—both serve as media for externalizing ideas, supporting memory retention, and enabling iterative exploration and refinement. However, unlike sketching’s linear and manually controlled flow, Gen-AI interactions often unfold in abrupt and nonlinear shifts. This research focuses on the triggering mechanisms of creative shifts, investigating how designers co-create with Gen-AI across multimodal workflows in early-stage architectural ideation.
To address a gap in the Architecture, Engineering, and Construction (AEC) domain, where Gen-AI tools are commonly evaluated by output fidelity or process efficiency rather than cognitive mechanisms, this study adopts a protocol-based analysis of design cognition. Eight participants with architectural training (two pilot and six main sessions) completed three consecutive 30-minute tasks based on a shared renovation scenario using Stable Diffusion and ControlNet via the LiblibAI platform. Each session was conducted under modality constraints: prompt-only, image/sketch-only (ControlNet-guided), and free multimodal interaction. Screen recordings, generated image sequences, and retrospective verbal protocols were collected for analysis.
The study extends Suwa et al.’s (1998) DCOCS framework by incorporating cross-cutting reaction tags: Wow, Good, and Fail, as cognitive trigger points. These were visualized through time-based and image-flow diagrams to enable cross-case comparison. Findings show that Fail reactions are most frequent, often forming iterative failure loops that trigger corrective strategies and modality switching; Wow reactions, though rare, consistently mark conceptual pivots that prompt reinterpretation or goal resets. These Wow moments resemble what Akin and Akin (1996) described as “Sudden Mental Insight” (SMI), a cognitive leap triggered by the restructuring of internal information.
Synthesizing these dynamics, the study identifies three distinct co-creation orientations: Machine-Led, Human-Led, and Hybrid (Human-Led–Machine-Led), demonstrating that creative outcomes depend less on tool fluency than on designers’ modal agility and interpretive agency under uncertainty. In doing so, this research contributes to the broader field of design cognition by illuminating the micro-level linkages between triggering events and strategic adaptation during human–AI interaction.
This thesis proposes a trigger-oriented cognitive account of human–Gen-AI co-creation, shifting the evaluation lens from output quality to the underlying generative dynamics. It also surfaces current interface limitations, such as disjointed creative rhythms, imprecise control over randomness, and restricted multimodal handoffs, that hinder smooth co-creation. Accordingly, the study offers concrete design and pedagogical recommendations: building interpretable input–output mappings to support sense-making, introducing tunable stochasticity to regulate exploratory uncertainty, and enabling seamless modality switching to preserve authorship and support reflective iteration in AI-assisted workflows.

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