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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2511.03375 |
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| _version_ | 1866909887840124928 |
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| author | Guo, Mengyao Nie, Kexin Gao, Ze Sun, Black Wang, Xueyang Han, Jinda Wu, Xingting |
| author_facet | Guo, Mengyao Nie, Kexin Gao, Ze Sun, Black Wang, Xueyang Han, Jinda Wu, Xingting |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_03375 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | I Prompt, it Generates, we Negotiate. Exploring Text-Image Intertextuality in Human-AI Co-Creation of Visual Narratives with VLMs Guo, Mengyao Nie, Kexin Gao, Ze Sun, Black Wang, Xueyang Han, Jinda Wu, Xingting Human-Computer Interaction 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. |
| title | I Prompt, it Generates, we Negotiate. Exploring Text-Image Intertextuality in Human-AI Co-Creation of Visual Narratives with VLMs |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2511.03375 |