Analiza uporabe velikih jezikovnih modelov in strategij pozivanja pri generativnem oblikovanju uporabniških vmesnikov

Tomovski, Eftimije

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Title:	Analiza uporabe velikih jezikovnih modelov in strategij pozivanja pri generativnem oblikovanju uporabniških vmesnikov
Authors:	ID Tomovski, Eftimije (Author) ID Šumak, Boštjan (Mentor) More about this mentor...
Files:	MAG_Tomovski_Eftimije_2026.pdf (4,53 MB) MD5: D953BDA02A7FD6BCF9CA334C16728E90
Language:	Slovenian
Work type:	Master's thesis/paper
Typology:	2.09 - Master's Thesis
Organization:	FERI - Faculty of Electrical Engineering and Computer Science
Abstract:	Generativno oblikovanje komponent uporabniških vmesnikov z uporabo velikih jezikovnih modelov je naraščajoče področje, ki kljub vse širši praktični uporabi ostaja metodološko slabo empirično raziskano. Magistrska naloga preučuje, kako kakovost pozivov in izbira sistema velikega jezikovnega modela vplivata na zaznano kakovost in prilagodljivost generiranih komponent. V okviru treh eksperimentov so bili primerjani sistemi ChatGPT, Claude Sonnet in Gemini pri treh nivojih kakovosti pozivov in različnih tipih komponent uporabniških vmesnikov. Evalvacija kakovosti je temeljila na okviru šestih metrik, evalvacija prilagodljivosti pa na treh dimenzijah, oblikovanih za namen te raziskave. Rezultati kažejo, da kakovost pozivov in izbira sistema vplivata na zaznano kakovost in prilagodljivost generiranih komponent, pri čemer se njun prispevek razlikuje glede na merjeno dimenzijo. Ugotovitve prispevajo k razvoju evalvacijske metodologije za generativno oblikovanje komponent in imajo neposredne implikacije za uvajanje generativnih orodij v procese oblikovanja programske opreme.
Keywords:	veliki jezikovni modeli, generativno oblikovanje vmesnikov, inženiring pozivov, evalvacija kakovosti, prilagodljivost komponent, primerjalna analiza sistemov, Nielsenove hevristike
Place of publishing:	Maribor
Year of publishing:	2026
PID:	20.500.12556/DKUM-98947
Publication date in DKUM:	24.09.2026
Views:	32
Downloads:	0
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Categories:	KTFMB - FERI
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Licences

License:	CC BY-ND 4.0, Creative Commons Attribution-NoDerivatives 4.0 International

Link:	http://creativecommons.org/licenses/by-nd/4.0/
Description:	Under the NoDerivatives Creative Commons license one can take a work released under this license and re-distribute it, but it cannot be shared with others in adapted form, and credit must be provided to the author.
Licensing start date:	21.07.2026

Secondary language

Language:	English
Title:	Analysis of the use of large language models and prompt strategies in generative user interface design
Abstract:	Generative design of user interface components using large language models is an emerging field that, despite its increasingly widespread practical adoption, remains methodologically underexplored from an empirical perspective. This master's thesis investigates how prompt quality and the choice of large language model system affect the perceived quality and adaptability of generated components. Across three experiments, the systems ChatGPT, Claude Sonnet, and Gemini were compared at three levels of prompt quality and across different types of user interface components. Quality evaluation was based on a framework of six metrics, while adaptability was evaluated using three dimensions specifically developed for this research. The results indicate that both prompt quality and system choice influence the perceived quality and adaptability of generated components, with their respective contributions varying depending on the dimension being measured. The findings contribute to the development of an evaluation methodology for the generative design of components and have direct implications for the adoption of generative tools in software design processes
Keywords:	large language models, generative UI design, prompt engineering, quality evaluation, component adaptability, comparative system analysis, Nielsen heuristics

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