As generative AI becomes part of everyday writing, questions of transparency and productive human effort are increasingly important. Educators, reviewers, and readers want to understand how AI shaped the process. Where was human effort focused? What role did AI play in the creation of the work? How did the interaction unfold? Existing approaches often reduce these dynamics to summary metrics or simplified provenance. We introduce DraftMarks, an augmented reading tool that surfaces the human-AI writing process through familiar physical metaphors. DraftMarks employs skeuomorphic encodings such as eraser crumbs to convey the intensity of revision, and masking tape or smudges to mark AI-generated content, simulating the process within the final written artifact. By using data from writer-AI interactions, DraftMarks' algorithm computes various collaboration metrics and writing traces. Through a formative study, we identified computational logic for different readership, and evaluated DraftMarks for its effectiveness in assessing AI co-authored writing.

翻译：随着生成式人工智能成为日常写作的一部分，透明度与人类有效投入的问题日益重要。教育工作者、审稿人和读者希望理解人工智能如何影响了写作过程。人类的努力集中在何处？人工智能在作品创作中扮演了何种角色？双方的互动是如何展开的？现有方法通常将这些动态简化为摘要性指标或简化的溯源信息。我们提出了DraftMarks，一种增强型阅读工具，它通过熟悉的物理隐喻来呈现人机协作的写作过程。DraftMarks采用拟物化编码，例如用橡皮屑表示修改的强度，用遮蔽胶带或污迹标记AI生成的内容，在最终成文作品中模拟创作过程。该工具利用作者与AI交互的数据，通过算法计算多种协作指标和写作痕迹。通过一项形成性研究，我们确定了面向不同读者群体的计算逻辑，并评估了DraftMarks在评估AI协同写作方面的有效性。