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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2024.05
- 수록면
- 41 - 52 (12page)
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이 논문의 연구 히스토리 (2)
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초록· 키워드
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This study explores AI-based prompt design methods and their impact on architectural design process and designers, examining how these approaches alter traditional design processes and redefine architects’ roles. By synthesizing the insights of six design practitioners, this study contrasts prompt design with conventional methods, exploring their practical applications and potential future directions. It also delves into the challenges and limitations posed by AI technologies in this evolving context. This study reveals that AI-based prompt design represents a new design paradigm, where prompted keywords play a central role in streamlining workflow, boosting creativity, and facilitating the translation of concepts into architectural schematics. This paradigm shift transforms architects from solitary designers into AI collaborators, necessitating a broader skill set in design literacy. Prompt design, driven by designer-suggested keywords or prompts, enables a more direct and precise process, allowing architects to clearly define their goals and scope. The evolving role of architects reflects this collaborative approach, with AI handling visualization tasks while designers focus on design decision-making. This shift underscores the ongoing relevance and expertise of architects, highlighting their new role as diversified engineering designers in a dynamic AI-driven environment.
목차
Abstract
1. 서론
2. 문헌고찰
3. 연구방법
4. 연구결과
5. 결과 논의
6. 결론
REFERENCES
참고문헌 (27)
참고문헌 신청
Eilouti, B. (2019). Shape grammars as a reverse engineering method for the morphogenesis of architectural façade design. Frontiers of Architectural Research, 8(2), 191-200.
Ghimire, P., Kim, K., & Acharya, M. (2024). Opportunities and challenges of generative AI in construction industry: focusing on adoption of text-based models. Buildings, 14(1), 17-21.
Huang, C., Zhang, G., Yao, J., Wang, X., Calautit, J. K., Zhao, C., An, N., & Peng, X. (2022). Accelerated environmental performance-driven urban design with generative adversarial network. Building and Environment, 224, 2-5. DOI:10.1016/j.buildenv.2022.109575
Jing, Y., Zhou, G., Zhang, C., Chang, F., Yan, H., & Xiao, Z. (2024). XMKR: Explainable manufacturing knowledge recommendation for collaborative design with graph embedding learning. Advanced Engineering Informatics, 59, 1-4, DOI:10.1016/j.aei.2023.102339
Ko, H. K., Park, G., Jeon, H., Jo, J., Kim, J., & Seo, J. (2023, March 27-31). Large-scale text-to-image generation models for visual artists’ creative works [Paper presentation]. IUI '23: 28th International Conference on Intelligent User Interfaces, Sydney, NSW, Australia.
Ghimire, P., Kim, K., & Acharya, M. (2024). Opportunities and challenges of generative AI in construction industry: focusing on adoption of text-based models. Buildings, 14(1), 17-21.
Huang, C., Zhang, G., Yao, J., Wang, X., Calautit, J. K., Zhao, C., An, N., & Peng, X. (2022). Accelerated environmental performance-driven urban design with generative adversarial network. Building and Environment, 224, 2-5. DOI:10.1016/j.buildenv.2022.109575
Jing, Y., Zhou, G., Zhang, C., Chang, F., Yan, H., & Xiao, Z. (2024). XMKR: Explainable manufacturing knowledge recommendation for collaborative design with graph embedding learning. Advanced Engineering Informatics, 59, 1-4, DOI:10.1016/j.aei.2023.102339
Ko, H. K., Park, G., Jeon, H., Jo, J., Kim, J., & Seo, J. (2023, March 27-31). Large-scale text-to-image generation models for visual artists’ creative works [Paper presentation]. IUI '23: 28th International Conference on Intelligent User Interfaces, Sydney, NSW, Australia.
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