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Abstract: Selective exposure in online news is often attributed to person- alization mechanisms and user modeling. Recent work proposes interface-level interventions that visualize reading balance or frame cross-cutting exposure as progress. However, we lack empirical evidence on whether alternative representations of user-model feedback meaningfully influence engagement with belief-opposing content. We conducted a between-subject experiment (N = 84) in a controlled news environment comparing two representations of diversity feedback: (1) an analytic bias visualization summarizing viewpoint balance and (2) a metaphorical growth visualization fram- ing cross-cutting exposure as personal development. Across behav- ioral and attitudinal measures of open-minded engagement, neither feedback representation increased engagement relative to control, and the two designs did not differ reliably. Our results suggest that lightweight representations of diversity signals—without adaptive personalization or structural changes to recommendations—may be insufficient to alter selective exposure in single-session settings. We discuss implications for designing user-model feedback and depolarization objectives in recommender systems.
Keywords: human-centered computing, human computer interaction, information systems, recommender systems
Published in RUP: 05.06.2026; Views: 640; Downloads: 36
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Abstract: This chapter provides an overview of the current state of radar-based Human-Computer Interaction (HCI) and outlines future research directions. While earlier chapters in this book have explored specialised domains, such as through-material sensing, ambient intelligence, surface context awareness, hand air-writing, and on-object gesture detection, this chapter expands this perspective by examining fifty carefully selected publications that address a broad range of design and technical considerations in radar-based HCI. We review key design choices, including gesture set definitions, sensor placements, radar types, frequency ranges, signal representations, and classification algorithms. Building on this foundation, the chapter then presents a forward-looking discussion of research opportunities. Particular emphasis is placed on the need for miniaturised, energy-efficient, and adaptive radar systems capable of functioning reliably in diverse, real-world settings. Additionally, the chapter stresses the importance of developing open, standardised datasets to support reproducibility, improve generalizability, and promote inclusive design.
Keywords: radar-based human-computer interaction, future directions, current state
Published in RUP: 19.05.2026; Views: 632; Downloads: 38
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Abstract: In recent years, miniature radar-on-chip sensors have been explored for HCI by both academia and industry. This is driven by the availability of affordable radar hardware and advances in signal processing and machine learning. However, comparative evaluation of radar-based gesture interaction systems is challanging and rear. One important dimension for comparison is the set of radar signal representa- tions derived from raw voltage data. These representations commonly include range- Doppler, range-azimuth-angle, range-elevation-angle, point cloud and In-phase and Quadrature (IQ) radar cube formats. However, existing studies often restrict com- parative analysis to a single signal representation type, typically focusing on gesture recognition algorithms or minor variations within Digital Signal Processing (DSP) pipelines. To promote comparative evaluation of radar signal representations, this chapter develops an open-source application designed to facilitate efficient and reli- able dataset preparation of various radar signal representations. The application sup- ports visualisation of generated signal representations and includes a command-line interface for batch processing, thereby streamlining the dataset preparation workflow.
Keywords: digital signal processing, radar, human-computer interaction
Published in RUP: 18.05.2026; Views: 648; Downloads: 8
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Abstract: Psychologists, specializing in work and organizational psychology (occupational psychologists), play a crucial role in the implementation and development of Human resource management (HRM) processes, which are increasingly affected by artificial intelligence (AI). This research aims to examine how and why AI is utilized among occupational psychologists, as well as the ethical dilemmas associated with this use in relation to the Code of Professional Ethics for Psychologists (the Code). The empirical part of this pilot study is based on a qualitative research design. 6 semi-structured interviews based on 11 questions regarding the use of AI in occupational psychologists’ work were conducted. Participants were selected through purposive sampling. The study results reveal that AI plays a vital role in automating routine administrative tasks. Participants highlighted its contribution to time efficiency and its utility in language translation. Nonetheless, concerns arise regarding increases in the occupational psychologists’ workload, as human oversight is required to ensure the accuracy and reliability of AI. While AI promises objectivity, participants cautioned that algorithms can be biased, potentially leading to discriminatory practices. They also expressed concerns about accountability. This question is pivotal, as the Code mandates the maintenance of professional boundaries and the respect for individuals' rights. Participants also pointed to risks associated with data protection. Despite this awareness, participants admitted they do not rely on the Code in their work with AI, which increases the risk of unethical decision-making. It is recommended that organizations provide adequate training for AI users, regulate data input, and ensure its use in accordance with ethical guidelines. The Slovenian Psychologists’ Association has a pivotal role in the preparation of specific guidelines and advising occupational psychologists on the ethical use of AI.
Keywords: artificial intelligence, AI integration in HRM, Code of professional ethics for psychologists, ethics in human resource processes, occupational psychologists, Slovenia
Published in RUP: 04.03.2026; Views: 797; Downloads: 13
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Abstract: The integration of generative AI into creative industries is reshaping how content is produced, evaluated, and distributed. While recent advancements offer new opportunities for automation and innovation, they also raise questions about authorship, authenticity, and professional identity. This study examines public discourse on generative AI in creative domains through a text-mining analysis of nearly 4000 Reddit posts and comments. Drawing on six relevant subreddits from 2022 to 2025, the research investigates the structure of user engagement, interaction dynamics, and language patterns. It identifies dominant terms and phrases related to AI creativity, explores thematic clusters, and compares discussion styles across key tools such as Midjourney, ChatGPT, Stable Diffusion, and DALL·E. Additionally, it provides a sentiment overview based on automated classification and narrative interpretation. The findings show that Reddit users engage with generative AI not only as a set of technical tools but as a source of cultural, ethical, and creative negotiation. This study contributes to a deeper understanding of how digital transformation in creative industries is shaped by public perception, platform discourse, and evolving community norms.
Keywords: artificial intelligence, generative artificial intelligence, creative industries, human–AI collaboration, creative labor, text mining, Reddit, business ecosystems, value creation
Published in RUP: 16.02.2026; Views: 1646; Downloads: 36
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