Quantitative Raw Data

This dataset contains the quantitative raw data from a comparative case study designed to validate the Synergistic AI-Driven Methodology (SADM) for dynamic poster design. The associated study investigates how structured human-machine collaboration frameworks can mitigate systemic workflow fragmentation and enhance efficiency when using generative AI tools.
The dataset records the workflow performance metrics and qualitative expert evaluation scores of 10 professional designers. It provides empirical evidence demonstrating that the SADM framework significantly reduces project cycle times while simultaneously increasing creative solution diversity compared to traditional, unstructured AI workflows.

本数据集包含一项比较案例研究的定量原始数据，该研究旨在验证面向动态海报设计的协同式人工智能驱动方法论（Synergistic AI-Driven Methodology，SADM）。相关研究探究了结构化人机协作框架在使用生成式AI（generative AI）工具时，可如何缓解系统性工作流碎片化问题并提升工作效率。
本数据集记录了10名专业设计师的工作流性能指标与定性专家评估得分。其提供的实证证据表明，相较于传统非结构化人工智能工作流，SADM框架可显著缩短项目周期，同时提升创意方案的多样性。