Sirvi Autor "Halas, Yana, juhendaja" 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.
Customer-Centric Redesign of the Mortgage Application Process in Baltic Banks
(Tartu Ülikool, 2025) Beisembayev, Merey; Milani, Fredrik Payman, juhendaja; Halas, Yana, juhendaja; Tartu Ülikool. Loodus- ja täppisteaduste valdkond; Tartu Ülikool. Arvutiteaduse instituut
Placing customers firmly at the center of the business underpins the main idea behind customer centricity. Previous studies have effectively indicated the benefits of integrating customer centricity into business processes, such as gaining a competitive advantage and increased customer satisfaction. However, the mortgage process remains understudied from a customer perspective, particularly in the context of the digital environment. This thesis investigates how Baltic banks can redesign their digital mortgage application process to be more customer-centric, focusing on the experiences and pain points of private mortgage clients in Estonia, Latvia, and Lithuania. For this purpose, the research adopted a qualitative approach to analyze collected data from competitor analysis of Baltic banks' mortgage webpages and semi-structured interviews with borrowers. The competitor analysis resulted in 40 content elements and features, identifying customer service gaps in how banks communicate mortgage information to prospective borrowers before application. Furthermore, through thematic analysis of the semi-structured interviews, we extracted 6 themes and 22 subthemes, characterizing customers' unmet needs, challenges, and overall experience dealing with the mortgage process. Finally, the empirical findings were synthesized to propose 12 actionable customer-centric recommendations for improving the customer-centricity of the mortgage process. This thesis provides a deeper insight into the mortgage experience of Baltic banks' borrowers and contributes to the implementation of customer-centric business redesign in the banking industry.
LLMs as a Tool for User Experience Research: A Comparison of Synthetic and Real-World Data
(Tartu Ülikool, 2025) Suslova, Viktorija; Halas, Yana, juhendaja; Eden, Grace, juhendaja; Tartu Ülikool. Loodus- ja täppisteaduste valdkond; Tartu Ülikool. Arvutiteaduse instituut
This thesis examines whether large language models (LLMs) can effectively support early-stage User Experience (UX) research by generating synthetic user data. The study compares two datasets within the domain of online food delivery services: one from five semi-structured interviews with women aged 35-45 in Tallinn, and another generated using ChatGPT following the same interview guide. Interviews from both datasets were thematically analysed, and the resulting personas and user scenarios were assessed through a side-by-side review focusing on emotional nuance, demographic accuracy, coherence, and the realism of goals, tasks, and interactions. Results show that LLMs can produce coherent, structured, and context-relevant outputs that capture common user concerns such as navigation, delivery timing, and order modifications. Synthetic data proved useful for generating plausible personas and scenarios quickly, offering advantages in speed, consistency, and thematic breadth. However, the outputs often lacked the emotional depth, context-specific details, and behavioural variability present in real-world data. LLMs tended to generalise user behaviour, repeat similar points across participants, and occasionally introduce plausible but unfounded “false positives” that could misdirect research. Persona and scenario comparisons reinforced these trends, showing fewer concrete tasks, omission of some steps, and the inclusion of elements not mentioned by real-world participants. The findings suggest that LLMs can complement, but not replace, traditional qualitative methods. They are best used to accelerate exploration and extend research reach when time, budget, or participant access is limited, provided outputs are refined and validated by human researchers to ensure they reflect genuine user needs.