Abstract
During product conceptualization, capturing the non-linear history and cognitive intent is crucial. Traditional sketching tools often lose this context. We introduce DIMES (Design Idea Management and Evolution capture System), a web-based environment featuring sGIT (SketchGit), a custom visual version control architecture, and Generative AI. sGIT includes AEGIS, a module using hybrid Deep Learning and Machine Learning models to classify six stroke types. The system maps Git primitives to design actions, enabling implicit branching and multi-modal commits (stroke data + voice intent). In a comparative study, experts using DIMES demonstrated a 160% increase in breadth of concept exploration. Generative AI modules generated narrative summaries that enhanced knowledge transfer; novices achieved higher replication fidelity (Neural Transparency-based Cosine Similarity: 0.97 vs. 0.73) compared to manual summaries. AI-generated renderings also received higher user acceptance (Purchase Likelihood: 4.2 vs 3.1). This work demonstrates that intelligent version control bridges creative action and cognitive documentation, offering a new paradigm for design education.
Abstract (translated)
在产品概念化阶段，捕捉非线性的历史记录和认知意图至关重要。传统的草图工具往往丧失了这一背景信息。我们引入了一种新的系统DIMES（设计构思管理和演变捕获系统），这是一个基于Web的环境，它包含了sGIT（SketchGit）——一种定制化的视觉版本控制系统架构以及生成式人工智能模块。 sGIT包含了一个名为AEGIS的模块，该模块使用混合深度学习和机器学习模型来分类六种笔画类型。系统将Git的基本操作映射到设计行动上，从而支持隐式分支和多模态提交（包括笔画数据和语音意图）。在一项比较研究中，专家们在使用DIMES后展示出了概念探索范围的160%的增长。 生成式人工智能模块还能够创建叙事总结，这些总结提升了知识转移的效果。新手通过AI生成的叙述获得的知识，比手动编写的叙述更易于理解和复制（基于神经透明度的余弦相似性：0.97 vs 0.73）。此外，由AI生成的设计草图也获得了更高的用户接受度（购买意愿评分：4.2 vs 3.1）。 这项工作表明，智能版本控制系统能够连接创意行动和认知文档记录，为设计教育提供了一种新的范式。
URL
https://arxiv.org/abs/2602.06047
