Sirvi Autor "Mustonen, Mattias-Silvester" järgi

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AI-enabled Prototyping Tools in User-centered Design
(Tartu Ülikool, 2025) Mustonen, Mattias-Silvester; Eden, Grace, juhendaja; Halas, Yana, juhendaja; Tartu Ülikool. Loodus- ja täppisteaduste valdkond; Tartu Ülikool. Arvutiteaduse instituut
Recent advancements in Artificial Intelligence (AI) and the rapid development of generative models have created opportunities to integrate AI into digital prototyping applications, thereby enhancing functionality and capabilities. This thesis examines the current landscape of AI-enabled prototyping tools and centers its investigation around three research questions: RQ 1: “What is the current landscape of AI-enabled prototyping tools?” RQ2: “How will users assess the quality and usability of an AI generated prototype?” RQ 3: “How can an AI-generated prototype be iteratively improved based on participant’s user feedback?” The findings indicate that the landscape of AI-supported prototyping tools remain in an early developmental phase, with only two applications—Visily and Uizard—demonstrating notable maturity in this domain. This suggests that these tools possess sufficient functionality and usability to be effectively employed in prototyping activities. An experiment was conducted using Visily to create a prototype of the Tartu Bike Sharing application. Participant evaluations revealed that, although the tool was capable of generating a functional and visually coherent prototype based upon prompts, it exhibited shortcomings in critical usability aspects (see section 5.3). Refinement efforts informed by user feedback highlighted limitations in the prototype’s editing functionality, as well as Visily’s insufficient ability to incorporate more complex elements, such as city maps. The primary contribution of this thesis lies in providing practical insights into the capabilities and limitations of AI in the context of digital prototyping. By identifying the scope of AI's potential, the required skill sets for their use in the design process can be further assessed, enabling opportunities to enhance efficiency and design quality. Given the rapid evolution of AI technologies, it is recommended that future research be conducted across different case studies.