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Enhancing generative user interfaces with LLMs: A user-driven iterative refinement process
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School of Science |
Master's thesis
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en
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96
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Abstract
Generative user interfaces can offer a personalized experience by adapting content and layout to individual preferences in real time. Recent advancements in large language models (LLMs) have demonstrated significant capabilities for dynamic and real-time user interface (UI) generation based on natural language prompts. However, existing solutions have primarily focused on user interface code generation for developers using large language models, while their practical usability and personalization capabilities for non-technical end users remain underexplored. This study investigates how users interact with a UI personalization system driven by OpenAI's GPT-4.1-nano model, integrated into a custom-built Android application, AdaptFit. This research aims to understand the user experience, the effectiveness of user involvement, the ease of user interface personalization, and the challenges users face in this UI personalization process.
This study combines both quantitative and qualitative methods, including questionnaires, usability testing with six participants, and semi-structured interviews. Thematic analysis was applied to better understand user experiences, and user iteration behaviors were recorded to examiner user satisfaction, prompt specificity, and outcome quality. Results show that users found the concept of UI personalization intriguing and engaging, but the performance of the system was inconsistent, often limited by vague prompts, LLM hallucination, and fixed system parsing structures. Specifically, well-articulated, detailed prompts yielded better outcomes, which shows the importance of prompt quality in LLM-driven design.
This thesis offers insights into the design of LLM-powered UI personalization system. Future work could explore better integration between generated outputs and UI framework to enhance real-world deployment.
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Supervisor
Nieminen, Mika P.Thesis advisor
Bogdan, CristianPayberah, Amir Hossein