In Situ AI Prototyping: Infusing Multimodal Prompts into Mobile Settings with MobileMaker
arxiv(2024)
摘要
Recent advances in multimodal large language models (LLMs) have lowered the
barriers to rapidly prototyping AI-powered features via prompting, especially
for mobile-intended use cases. Despite the value of situated user feedback, the
process of soliciting early, mobile-situated user feedback on AI prototypes
remains challenging. The broad scope and flexibility of LLMs means that, for a
given use-case-specific prototype, there is a crucial need to understand the
wide range of in-the-wild input likely to be provided by the user, as well as
their in-context expectations of the AI's behavior. To explore the concept of
in situ AI prototyping and testing, we created MobileMaker: an AI prototyping
tool that enables designers to rapidly create mobile AI prototypes that can be
tested on-device, and enables testers to make on-device, in-the-field revisions
of the prototype through natural language. In an exploratory study with 16
users, we explored how user feedback on prototypes created with MobileMaker
compares to that of existing prototyping tools (e.g., Figma, prompt editors).
We found that MobileMaker prototypes enabled more serendipitous discovery of:
model input edge cases, discrepancies between AI's and user's in-context
interpretation of the task, and contextual signals missed by the AI.
Furthermore, we learned that while the ability to make in-the-wild revisions
led users to feel more fulfilled as active participants in the design process,
it might also constrain their feedback to the subset of changes perceived as
more actionable or implementable by the prototyping tool.
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