我提示，它生成，我们协商。探索使用VLMs在人类与AI共创视觉叙事中的文本-图像互文性
I Prompt, it Generates, we Negotiate. Exploring Text-Image Intertextuality in Human-AI Co-Creation of Visual Narratives with VLMs
AI总结：
本研究通过GPT-4o对15名参与者进行定性分析，揭示了人类与AI共创视觉叙事中的文本-图像互文性，识别出四种协作模式与三条成功路径，并提出了基于角色的AI助手设计启示。
摘要：
通过人类与AI协作创作有意义的视觉叙事，需要理解当文本意图遇到AI生成的视觉内容时，文本-图像互文性是如何产生的。我们对15名参与者使用GPT-4o进行了三阶段的定性研究，以调查新手如何驾驭顺序视觉叙事。研究结果表明，用户通过识别超越字面描述的有意义的视觉内容、迭代细化提示词，以及通过互补的文本-图像关系构建叙事意义，制定了利用AI语义盈余的策略。我们识别出四种不同的协作模式，并通过fsQCA分析，发现了实现成功互文协作的三条路径：教育协作者、技术专家和视觉思考者。然而，参与者面临挑战，包括文化表征差距、视觉一致性问题，以及将叙事概念转化为视觉提示词的困难。这些发现为HCI研究做出了贡献，提供了人类与AI共创中“文本-图像互文性”的实证描述，并提出了基于角色的AI助手的设计启示，以更好地支持视觉叙事中迭代的、人类主导的创意过程。
英文摘要：
Creating meaningful visual narratives through human-AI collaboration requires understanding how text-image intertextuality emerges when textual intentions meet AI-generated visuals. We conducted a three-phase qualitative study with 15 participants using GPT-4o to investigate how novices navigate sequential visual narratives. Our findings show that users develop strategies to harness AI's semantic surplus by recognizing meaningful visual content beyond literal descriptions, iteratively refining prompts, and constructing narrative significance through complementary text-image relationships. We identified four distinct collaboration patterns and, through fsQCA's analysis, discovered three pathways to successful intertextual collaboration: Educational Collaborator, Technical Expert, and Visual Thinker. However, participants faced challenges, including cultural representation gaps, visual consistency issues, and difficulties translating narrative concepts into visual prompts. These findings contribute to HCI research by providing an empirical account of \textit{text-image intertextuality} in human-AI co-creation and proposing design implications for role-based AI assistants that better support iterative, human-led creative processes in visual storytelling.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
