| 摘要: |
| 目的 从设计学视角探讨如何利用人工智能技术辅助创意激发。方法 以提高创意激发水平为导向，共召集18名设计专业学生分次使用传统搜索和文生图AI来进行用户体验测试，通过量表、访谈及非参与式观察收集定量定性数据进行模式分析，验证人工智能辅助创意激发的有效性。结果 人工智能辅助可以有效降低脑力负荷，减轻工作负担，支持设计师在创意探索和表达方面的活动。然而，与参与者主观感受相悖，人工智能实际未能显著提高参与者设计输出的新颖性和质量。对人工智能辅助下设计师行为特征进行分析，提炼出人工智能生成内容(AIGC)辅助创意激发的2种模式，代理型用户“委托-采纳”行为模式，表现为“委托主体-接纳偏差-采纳结果”;协同型用户“辅助-迭代”行为模式，表现为“独立拆解-排斥偏差-反复迭代”。结论 用户实验验证了人工智能在辅助创意激发上的能力，归纳了2类设计师的行为模式及成因，为后续人工智能赋能传统设计流程提供了新的研究思路。 |
| 关键词: 人工智能 创意激发 生成式人工智能(AIGC) 辅助设计 |
| DOI：10.19554/j.cnki.1001-3563.2025.10.003 |
| 分类号: |
| 基金项目:文旅部国家文化和旅游科技创新研发项目;教育部社科基金规划基金项目(23YJA760090) |
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| Pattern of Creativity Inspiration Assisted by Artificial Intelligence Generated Content (AIGC) |
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WANG Xiaohui, TIAN Tianhong, LI Jinyu
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(School of Mechanical Engineering, University of Science and Technology Beijing, Beijing 100083, China)
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| Abstract: |
| The work aims to study ways of using the artificial intelligence technology to assist in inspiring creativity from the perspective of design. To enhance the efficacy of creativity inspiration, 18 design students were recruited to use traditional search and Text to Image AI for user experience testing. Quantitative and qualitative data were collected through scales, interviews and non-participatory observation for pattern analysis to verify the effectiveness of artificial intelligence in assisting creativity inspiration. The results indicated that AI could effectively reduce cognitive load and support designers in their exploratory and expressive activities. However, despite reducing the workload, AI assistance did not significantly improve the novelty or quality of the design output, which was contrary to the subjective feelings of the participants. Furthermore, by analyzing the behavioral characteristics of designers under AI assistance, the designers were categorized into two types:Agentive and Collaborative. The Agentive users adopted the "Delegate-Adoption" Pattern, which was manifested as "Commissioning - Accepting - Adopting"; and the Collaborative users adopted the "Assistive-Iterative" Pattern, which was manifested as "Dismantling - Rejecting - Iterating". Through the user experiment test, this research analyzes the capability of artificial intelligence in assisting creativity inspiration, and summarizes the behavioral logic and reasons for these classifications, providing insights for subsequent studies on integrating AI into traditional design workflows. |
| Key words: artificial intelligence creativity inspiration artificial intelligence generated content (AIGC) design assistance |
