Generative AI brings novel and impressive abilities to help people in everyday tasks. There are many AI workflows that solve real and complex problems by chaining AI outputs together with human interaction. Although there is an undeniable lure of AI, it is uncertain how useful generative AI workflows are after the novelty wears off. Additionally, workflows built with generative AI have the potential to be easily customized to fit users' individual needs, but do users take advantage of this? We conducted a three-week longitudinal study with 12 users to understand the familiarization and customization of generative AI tools for science communication. Our study revealed that there exists a familiarization phase, during which users were exploring the novel capabilities of the workflow and discovering which aspects they found useful. After this phase, users understood the workflow and were able to anticipate the outputs. Surprisingly, after familiarization the perceived utility of the system was rated higher than before, indicating that the perceived utility of AI is not just a novelty effect. The increase in benefits mainly comes from end-users' ability to customize prompts, and thus potentially appropriate the system to their own needs. This points to a future where generative AI systems can allow us to design for appropriation.

翻译：生成式AI为人们在日常任务中提供了新颖且令人印象深刻的能力。许多AI工作流程通过将AI输出与人类交互串联起来，解决真实而复杂的问题。尽管AI具有不可否认的吸引力，但生成式AI工作流程在新奇感消退后的实际效用尚不明确。此外，基于生成式AI构建的工作流程具有易于适应用户个性化需求的定制潜力，但用户是否会充分利用这一特性？我们开展了为期三周的纵向研究，邀请12名用户参与，旨在探究生成式AI工具在科学传播领域的熟悉过程与定制化实践。研究发现存在一个熟悉化阶段，在此期间用户探索工作流程的新颖功能，并逐步识别出对其有价值的方面。该阶段结束后，用户能够理解工作流程并预判其输出结果。值得注意的是，熟悉化后用户对系统效用的评价反而高于初期，这表明AI的感知效用并非仅源于新奇效应。效用提升主要源于终端用户定制提示词的能力，从而可能将系统适配至个人需求。这预示着生成式AI系统未来将支持我们设计出可供用户自主适配的技术方案。