Exploring the Promise and Peril of Generative AI for Usable Scientific GUIs

Generative AI tools are rapidly impacting software development practices including the development of graphical user interfaces (GUI). These coding and design tools offer an attractive promise of accelerating the pace of development while reducing onerous or mundane engineering tasks. Generative AI provides Research Software Engineers (RSEs) an opportunity to design and implement the graphical user interface (GUI) of scientific web applications rapidly, even when they may not be trained in web development. Generative AI tools such as Cursor [1], Builder.io [2], Lovable [3] and others, take a prompt (e.g. “I need a scientific data dashboard to monitor experiments and their data outputs”) and rapidly produce an attractive, seemingly useful interactive design that looks like it can be connected to a developer’s pre-existing application. However, the question is whether this approach will truly be able to help produce usable and meaningful GUIs. RSEs often do not have substantial training in user experience design and usability evaluation work. It is essential that RSEs leveraging AI tools to create UIs have resources that can provide them with confidence in the quality of the resulting user experience. The STRUDEL project [4] is dedicated to building open source scientific user experience (UX) resources, which incorporate expert UX researcher and designer knowledge that RSEs and domain scientists can easily leverage. Currently the project team is investigating the effectiveness of various AI tools to generate deployable prototypes for scientific research web applications. Upon initial exploration, we find that while AI tools can generate useful ideas and prototypes, they often require significant refinement, revision and expertise to generate usable and maintainable UIs for scientific software. To help improve the usability of the prototypes generated through AI tools, we experimented with using our STRUDEL design templates as starting points from which the AI tools can expand upon. Our experiments show the promise of using carefully researched templates as a type of guardrail for the AI tools. These guardrails can help ensure that the UI product adheres to certain design guidelines without the need to explicitly provide those details in a prompt. We posit that the use of human-designed seed templates as starting points can be a useful method for improving the overall usability of AI-generated GUIs. Using a seed template is a much-needed way to incorporate user experience expertise in the loop while leveraging AI tools to generate UI flows and patterns. Our initial work explores the implications of AI tool-generation on the usability, development, and long term maintenance of the GUIs, but it is clear this will be an important consideration for the US-RSE community. With this poster we aim to foster discussion among the US-RSE community to delve into the grand promises and likely perils of generative AI tools for building usable scientific web applications.