Authors' Values and Attitudes Towards AI-bridged Scalable Personalization of Creative Language Arts
Taewook Kim, Hyomin Han, Eytan Adar, Matthew Kay, John Joon Young Chung
摘要
Generative AI has the potential to create a new form of interactive media: AI-bridged creative language arts (CLA), which bridge the author and audience by personalizing the author's vision to the audience's context and taste at scale. However, it is unclear what the authors' values and attitudes would be regarding AI-bridged CLA. To identify these values and attitudes, we conducted an interview study with 18 authors across eight genres (e.g., poetry, comics) by presenting speculative but realistic AI-bridged CLA scenarios. We identified three benefits derived from the dynamics between author, artifact, and audience: those that 1) authors get from the process, 2) audiences get from the artifact, and 3) authors get from the audience. We found how AI-bridged CLA would either promote or reduce these benefits, along with authors' concerns. We hope our investigation hints at how AI can provide intriguing experiences to CLA audiences while promoting authors' values.
• Human-centered computing → Empirical studies in HCI.
问问这篇 Paper
智能体会读完全文。
Lune 把这篇 Paper 索引到了每一个公式,引用它的顶会 Paper 也一样。你提问,回答直接引用原文。
引用它的顶会 Paper16
- How CO2STLY Is CHI? The Carbon Footprint of Generative AI in HCI Research and What We Should Do About ItNanna Inie, Jeanette Falk, Raghavendra SelvanCHI 2025 · 被引用 33 次
- Co-Writing with AI, on Human Terms: Aligning Research with User Demands Across the Writing ProcessMohi Reza, Jeb Thomas-Mitchell, Peter Dushniku, Nathan Laundry 等CSCW 2025 · 被引用 29 次
- Patchview: LLM-powered Worldbuilding with Generative Dust and Magnet VisualizationJohn Joon Young Chung, Max KreminskiUIST 2024 · 被引用 29 次
- WhatELSE: Shaping Narrative Spaces at Configurable Level of Abstraction for AI-bridged Interactive StorytellingZhuoran Lu, Qian Zhou, Yi WangCHI 2025 · 被引用 25 次
- Understanding Screenwriters' Practices, Attitudes, and Future Expectations in Human-AI Co-CreationYuying Tang, Haotian Li, Minghe Lan, Xiaojuan Ma 等CHI 2025 · 被引用 16 次
它引用的顶会 Paper21
- Language Models are Few-Shot LearnersTom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah 等NeurIPS 2020 · 被引用 64,255 次
- Training language models to follow instructions with human feedbackLong Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida 等NeurIPS 2022 · 被引用 24,707 次
- High-Resolution Image Synthesis with Latent Diffusion ModelsRobin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser 等CVPR 2022 · 被引用 13,123 次
- Generative Agents: Interactive Simulacra of Human BehaviorJoon Sung Park, Joseph C. O'Brien, Carrie Jun Cai, Meredith Ringel Morris 等UIST 2023 · 被引用 1,882 次
- A Watermark for Large Language ModelsJohn Kirchenbauer, Jonas Geiping, Yuxin Wen, Jonathan Katz 等ICML 2023 · 被引用 854 次
相关 Paper
- AI in Webtoon Creation: Challenges, Perceptions, and Design ImplicationsSoomin Kim, Hyeryung ChungCHI 2026 · 被引用 1 次
- Generative AI in the Wild: Prospects, Challenges, and StrategiesYuan Sun, Eunchae Jang, Fenglong Ma, Ting WangCHI 2024 · 被引用 63 次
- Can Good Writing Be Generative? Expert-Level AI Writing Emerges through Fine-Tuning on High Quality BooksTuhin Chakrabarty, Paramveer S. DhillonCHI 2026 · 被引用 2 次
- Generative AI as a Mediator in Creator Collaboration: Challenges, Design Opportunities, and ConcernsHajun Kim, Jini Kim, Yunjae Josephine ChoiCSCW 2026
- "I Just Don't Want My Work Being Fed Into The AI Blender": Queer Artists on Refusing and Resisting Generative AIJordan Taylor, Joel Mire, Alicia DeVrio, Maarten Sap 等CSCW 2026
