1. Prompt to press : evaluating human perception of AI involvement in news writing across prompt specificityUroš Sergaš, Ahmadou Wagne, Thomas Elmar Kolb, Julia Neidhardt, Bruce Ferwerda, Marko Tkalčič, 2026, objavljeni znanstveni prispevek na konferenci Opis: Large language models (LLMs) are becoming a common feature in content creation tools, prompting important questions about how design choices influence user trust and engagement in AI- assisted journalism. Beyond output quality, factors such as prompt specificity, model choice, and authorship disclosure are themselves interaction design parameters that influence how users interpret and evaluate AI contributions. Yet, little is known about how these design decisions affect reader perceptions in journalistic contexts. To address this gap, we conducted an experiment with 150 participants who evaluated news articles on the sensitive topic of assisted suicide. The articles systematically varied in authorship (human-written, AI-edited, or AI-generated), stance (pro- or anti- legalization), and prompt specificity (vague, moderate, or highly detailed). Participants rated each article on engagement, subjectivity, and perceived AI involvement, and also provided open-ended justifications for their authorship judgments. Our findings show that prompt specificity and model choice significantly influence perceptions of authorship, underscoring how technical design decisions in AI tools can shape public trust in journalism.
Ključne besede: AI-generated news, prompt specificity, human vs. AI detection, media perception, assisted suicide
Objavljeno v RUP: 24.03.2026; Ogledov: 405; Prenosov: 17
 Celotno besedilo (593,55 KB)
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2. Designing the ideal political identity questionnaire using machine learning and ideology scalesAna Nikolić, Uroš Sergaš, Marko Tkalčič, 2025, objavljeni znanstveni prispevek na konferenci (vabljeno predavanje) Opis: Political ideology shapes beliefs, behavior and attitudes toward society. Many existing questionnaires for measuring ideology are lengthy, repetitive, and misaligned with self-perception. This paper investigates whether a shorter, reliable, two-dimensional political identity questionnaire can be created using machine learning and psychometric methods. Sixty participants completed four ideological instruments (MFQ, SDO, RWA and 8values). Lasso regression and Random Forest with nested cross-validation identified predictive items, while psychometric evaluation included CFA and Cronbach’s alpha. Random Forest outperformed Lasso. Internal reliability was excellent and factor loadings supported a two-factor structure despite moderate model it. Findings show that ideology can be measured efficiently with reduced items, supporting applications in research, digital platforms and political psychology.
Ključne besede: political ideology, questionnaire design, machine learning, psychometrics
Objavljeno v RUP: 30.01.2026; Ogledov: 471; Prenosov: 3
Celotno besedilo (189,01 KB) |
3. Analiza vpliva tehnologije na kakovost spanja : zaključna nalogaTamara Mikač, 2025, diplomsko delo Ključne besede: kakovost spanja, modra svetloba, tehnologija, spalna rutina, življenski slog, napovedni modeli, stres, diplomske naloge
Objavljeno v RUP: 14.10.2025; Ogledov: 805; Prenosov: 120
Celotno besedilo (1,20 MB) |
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