1. AI Literacy Among University Students : a comparative study of three countries—Slovenia, Croatia, and IndiaMaja Rožman, Dijana Oreški, Arun A. Elias, Minnu F. Pynadath, Polona Tominc, 2025, original scientific article Abstract: This study investigates artificial intelligence (AI) literacy among university students from Slovenia, Croatia, and India, focusing on variations in their understanding of AI concepts and applications across different educational systems. The sample comprised 471 students from the business studies programmes in Slovenia, Croatia and India. Data were collected through a structured online questionnaire, and statistical analyses utilized descriptive statistics and independent samples proportions to identify significant differences in AI literacy levels across the three countries. The findings reveal substantial disparities in AI literacy. Croatian students demonstrated foundational and practical knowledge of AI, reflecting well-developed educational frameworks. Indian students excelled in interdisciplinary and applied aspects of AI, though their understanding of theoretical concepts revealed notable gaps. Slovenian students showed the greatest need for improvement, particularly in foundational knowledge, practical applications, and ethical considerations. The results also highlight universal challenges, such as gaps in understanding AI’s ethical and legal implications, underscoring the need for all educational systems to incorporate discussions on accountability and societal impacts into their curricula. These findings offer actionable recommendations for educational policy and curriculum design to enhance AI literacy globally and prepare students for the challenges and opportunities of an AI-driven world. This study contributes to the literature by enhancing the understanding of how AI literacy manifests across diverse educational systems, providing comparative insights to support globally relevant curriculum development and digital skills advancement.
Keywords: AI literacy, students, higher education, artificial intelligence
Published in DKUM: 05.11.2025; Views: 0; Downloads: 0
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2. Public attitudes towards artificial intelligence and its applicationsTomaž Gjergjek, 2025, master's thesis Abstract: In this master’s thesis, we researched how people perceive artificial intelligence (AI) and the factors that shape their attitudes. Understanding public attitudes toward AI is crucial for ensuring its responsible development and deployment, aligning technological progress with societal expectations, and addressing ethical and social concerns.
The primary goal of this study was to gain a comprehensive understanding of how the public forms attitudes toward AI and how these are influenced by perceived threats, societal benefits, and trust in institutions involved in AI development. The theoretical part reviewed the literature on the foundations of attitudes and on public perceptions of artificial intelligence, providing an essential framework for the empirical analysis.
In the empirical part, a survey was conducted to explore public attitudes toward AI. Respondents were asked about their perceptions of AI as a potential threat, its societal benefits, their level of trust in institutions and companies responsible for AI, and their overall stance toward the technology. The results revealed that individuals who perceive AI as a threat to their jobs or privacy tend to hold significantly more negative attitudes. In contrast, perceived societal benefits and institutional trust did not show a statistically significant impact on overall attitudes toward AI.
These findings highlight that personal concerns play a greater role than broader societal considerations or institutional trust. They also underline the importance of addressing individual fears and enhancing transparency in the development and deployment of AI technologies. Although the study faces limitations, such as a non-representative sample and reliance on self-reported survey data, it provides important insights into the complexity of public attitudes toward AI.
Future research should include longitudinal and cross-cultural studies, as well as a combination of quantitative and qualitative methods, to capture the dynamic and multifaceted nature of public perceptions. The study contributes to a deeper understanding of how AI is perceived and emphasizes the need for a responsible, ethical, and socially sensitive approach to its integration into society.
Keywords: artificial intelligence (AI), public attitudes, perceived threats, societal benefits, trust in institutions, ethical considerations
Published in DKUM: 30.10.2025; Views: 0; Downloads: 2
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3. Artificial intelligence-based approaches for advance care planning : a scoping reviewUmut Arioz, Matthew John Allsop, William D. Goodman, Suzanne Timmons, Kseniya Simbirtseva, Izidor Mlakar, Grega Močnik, 2025, review article Abstract: Background Advance Care Planning (ACP) empowers individuals to make informed decisions about their future healthcare. However, barriers including time constraints and a lack of clarity on professional responsibilities for ACP hinder its implementation. The application of artificial intelligence (AI) could potentially optimise elements of ACP in practice by, for example, identifying patients for whom ACP may be relevant and aiding ACP-related decision-making. However, it is unclear how applications of AI for ACP are currently being used in the delivery of palliative care. Objectives To explore the use of AI models for ACP, identifying key features that influence model performance, transparency of data used, source code availability, and generalizability. Methods A scoping review was conducted using the Arksey and O’Malley framework and the PRISMA-ScR guidelines. Electronic databases (Scopus and Web of Science (WoS)) and seven preprint servers were searched to identify published research articles and conference papers in English, German and French for the last 10Â years’ records. Our search strategy was based on terms for ACP and artificial intelligence models (including machine learning). The GRADE approach was used to assess the quality of included studies. Results Included studies (N = 41) predominantly used retrospective cohort designs and real-world electronic health record data. Most studies (n = 39) focused on identifying individuals who might benefit from ACP, while fewer studies addressed initiating ACP discussions (n = 10) or documenting and sharing ACP information (n = 8). Among AI and machine learning models, logistic regression was the most frequent analytical method (n = 15). Most models (n = 28) demonstrated good to very good performance. However, concerns remain regarding data and code availability, as many studies lacked transparency and reproducibility (n = 17 and n = 36, respectively). Conclusion Most studies report models with promising results for predicting patient outcomes and supporting decision-making, but significant challenges remain, particularly regarding data and code availability. Future research should prioritize transparency and open-source code to facilitate rigorous evaluation. There is scope to explore novel AI-based approaches to ACP, including to support processes surrounding the review and updating of ACP information.
Keywords: advance care planning, digital tools, palliative care, artificial intelligence, machine learning
Published in DKUM: 29.10.2025; Views: 0; Downloads: 4
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4. Machine learning in antiviral drug designAnja Kolarič, Marko Jukič, Urban Bren, 2026, review article Abstract: Viral infections pose a significant health threat worldwide. Due to the high mutation rates of many viruses and their reliance on host cellular machinery, the development of effective antiviral therapies is particularly difficult. As a result, only a limited number of antiviral agents is currently available. In parallel to modern vaccines, traditional antiviral drug development is both time-consuming and costly, underscoring the need for faster, more efficient approaches. In recent years, particularly since the beginning of the COVID-19 pandemic, machine learning (ML) together with broader artificial intelligence (AI), have emerged as powerful methodologies for drug discovery and offer the potential to accelerate the identification and development of antiviral agents. This review examines the application of ML in the early stages of antiviral drug discovery, with a particular focus on recent studies where ML methods have successfully identified hit compounds with experimentally demonstrated activity in biological assays. By highlighting these successful case studies, the review illustrates the growing impact of ML in advancing the discovery of urgently needed novel antivirals.
Keywords: machine learning, artificial intelligence, antiviral compounds, biological activity
Published in DKUM: 17.10.2025; Views: 0; Downloads: 3
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5. What can artificial intelligence do for soil health in agriculture?Stefan Schweng, Luca Bernardini, Katharina Keiblinger, Peter Kaul, Iztok Fister, Niko Lukač, Javier Del Ser, Andreas Holzinger, 2025, review article Abstract: The integration of artificial intelligence (AI) into soil research presents significant opportunities to advance the understanding, management, and conservation of soil ecosystems. This paper reviews the diverse applications of AI in soil health assessment, predictive modeling of soil properties, and the development of pedotransfer functions within the context of agriculture, emphasizing AI’s advantages over traditional analytical methods. We identify soil organic matter decline, compaction, and biodiversity loss as the most frequently addressed forms of soil degradation. Strong trends include the creation of digital soil maps, particularly for soil organic carbon and chemical properties using remote sensing or easily measurable proxies, as well as the development of decision support systems for crop rotation planning and IoT-based monitoring of soil health and crop performance. While random forest models dominate, support vector machines and neural networks are also widely applied for soil parameter modeling. Our analysis of datasets reveals clear regional biases, with tropical, arid, mild continental, and polar tundra climates remaining underrepresented despite their agricultural relevance. We also highlight gaps in predictor–response combinations for soil property modeling, pointing to promising research avenues such as estimating heavy metal content from soil mineral nitrogen content, microbial biomass, or earthworm abundance. Finally, we provide practical guidelines on data preparation, feature extraction, and model selection. Overall, this study synthesizes recent advances, identifies methodological limitations, and outlines a roadmap for future research, underscoring AI’s transformative potential in soil science.
Keywords: artificial intelligence, machine learning, agriculture, soil health, soil parameter modeling, regional data bias
Published in DKUM: 17.10.2025; Views: 0; Downloads: 1
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6. Drivers and constraints of employee satisfaction with remote work : an empirical analysisThabit Atobishi, Saeed Nosratabadi, 2023 Abstract: Background/Purpose: The Covid 19 epidemic has forced many organizations to move to remote work (RW), and this trend is expected to continue even later in the post-epidemic period. Employees of the organization are at the heart of this transi-tion to RW, so identifying the factors that affect employee satisfaction with RW is very important for organizations to increase employee commitment and motivation. Therefore, the main objective of this study was to identify and prioritize the factors affecting employee satisfaction with RW using an innovative method. Method: In the first phase of this study, a conceptual research model was designed inspired by literature. In the next phase, the proposed conceptual model of this re-search was tested using structural equation modeling (SEM). Then, using the artifi-cial neural network model, the importance of each of the model variables in pre-dicting employee satisfaction with RW was identified. Results: The findings of this article ultimately disclosed that work-life balance, in-stitutional and technological support, job satisfaction, and perceived limited com-munication are, respectively, are elements that affect employee satisfaction with RW. The first three factors are drivers of employee satisfaction and the last factor (i.e., perceived limited communication) is the constraint of employee satisfaction with RW because it had a statistically significant negative effect on employee satis-faction with RW. Conclusion: This study revealed that organizations should focus on the processes and strategies to improve employees’ work-life balance, provide institutional and technological support during remote work, and increase job satisfaction in order to increase the satisfaction level of their employees in the remote work. On the other hand, it was found that perceived limited communication is an effective factor that causes a decrease in the level of satisfaction of employees in remote work.
Keywords: remote work, employee satisfaction, structural equation modeling, multilayer per-ceptron, artificial intelligence, artificial neurol network, Covid 19 pandemic
Published in DKUM: 08.10.2025; Views: 0; Downloads: 0
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7. The impact of artificial intelligence in business communication on social networks: a case studyBlaž Kovač, 2025, undergraduate thesis Abstract: Artificial intelligence has revolutionised the way we communicate in recent years, especially on social networks, where communication is fast and large-scaled. Companies are also using artificial intelligence to communicate with customers, including automating messages intended for customer acquisition, which raises a key question: how effective are messages created with artificial intelligence compared to messages written without artificial intelligence? This thesis analyses the impact of artificial intelligence on business communication using the example of company X. The research compares the effectiveness of messages created with artificial intelligence with messages written without artificial intelligence in the form of LinkedIn outreach, focusing on two key performance indicators: the response rate to the messages and the number of video calls made with potential customers. In addition, the time efficiency of preparing content for messages with and without artificial intelligence was also analysed.
The results show that messages created with artificial intelligence were not only faster to prepare but also achieved higher response rates and a higher number of scheduled video calls with customers compared to messages written without artificial intelligence. This thesis contributes to a better understanding of the role of artificial intelligence in modern business communication and offers insights into its value in connecting with potential customers via social networks, using a practical example.
Keywords: Artificial intelligence, business communication, social networks, comparison.
Published in DKUM: 03.10.2025; Views: 0; Downloads: 13
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8. Protection of workers in relation to the use of artificial intelligence in the workplaceAsja Lešnik, 2025, original scientific article Abstract: This article examines the impact of artificial intelligence (AI) on all stages of the employment relationship and analyses whether the current legal framework adequately protects workers from the risks posed by the use of AI in the workplace. The focus is on Slovenian labour law, while also considering relevant international and EU legal sources such as the AI Act, the Directive on Improving Working Conditions in Platform Work, the GDPR, and the EU Charter of Fundamental Rights. The author addresses legal challenges including discrimination, data protection, privacy, occupational safety and health, and liability for damages. The article finds that while some protective mechanisms already exist, none of the analysed legal sources comprehensively regulate AI use in employment relationships. To ensure effective worker protection, the author argues for either the amendment of current laws or the adoption of dedicated legislation. Since AI will play an even more significant role in Labour Law in the future, it is crucial for the law to adapt in a timely manner to the new challenges posed by AI.
Keywords: artificial intelligence, algorithmic management, automation of work processes, discrimination, data protection, privacy protection, occupational safety and health, liability, worker protection, legal framework
Published in DKUM: 02.10.2025; Views: 0; Downloads: 4
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9. The role of intelligent data analysis in selected endurance sports : a systematic literature reviewAlen Rajšp, Patrik Rek, Peter Kokol, Iztok Fister, 2025, review article Abstract: In endurance sports, athletes and coaches shift increasingly from intuition-based decisionmaking to data-driven approaches powered by modern technology and analytics. Since 2018, the field has experienced significant advances, influencing endurance sports disciplines. This systematic literature review identified 75 peer-reviewed studies on intelligent data analysis in endurance sports training. Each study was categorized by its intelligent method (e.g., machine learning, deep learning, computational intelligence), the types of sensors and wearables used, and the specific training application and approach. Our synthesis reveals that machine learning and deep learning are among the most used approaches, with running and cycling identified as the most extensively studied sports. Physiological and environmental data, such as heart rate, biomechanical signals, and GPS, are often used to aid in generating personalized training plans, predicting injuries, and increasing athletes’ long-term performance. Despite these advancements, challenges remain, related to data quality and the small participant sample sizes.
Keywords: smart sports training, endurance sports, intelligent data analysis, machine learning, artificial intelligence, computational intelligence, systematic literature review
Published in DKUM: 02.10.2025; Views: 0; Downloads: 5
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10. Enhancing student motivation and engagement through the use of a Slovenian-speaking social robot AlphaMiniDaniel Hari, Vesna Skrbinjek, Andrej Flogie, 2025, original scientific article Abstract: The integration of Artificial Intelligence into education is transforming how abstract and complex concepts are delivered, especially through embodied tools like social robots. This study examines the impact of AlphaMini, a Slovenian-speaking social robot supported by model ChatGPT 4o and trained on structured book-based content, on student engagement during knowledge management lessons. A case study approach was used, including student questionnaires, classroom observations, and post-session discussions, with 70 university students from diverse academic fields. Engagement was assessed across behavioral, emotional, and cognitive dimensions, with comparisons based on prior robot experience. Results show AlphaMini significantly enhanced emotional and behavioral engagement, with moderate cognitive gains. Students familiar with social robots demonstrated higher engagement, interacting more naturally and actively. Informal feedback highlighted positive attitudes toward AlphaMini, especially among students who regularly use generative AI tools like ChatGPT or Copilot. Participants appreciated its human-like gestures, Slovenian language use, and emotionally supportive presence. Many suggested its potential use in primary and inclusive education, where emotional safety and playful interaction are crucial. This study contributes to the growing evidence on AI in education, showing that combining generative AI with social robotics can foster motivation, participation, and emotionally rich learning experiences.
Keywords: artificial intelligence, social robot, ChatGPT, student motivation, education
Published in DKUM: 18.09.2025; Views: 0; Downloads: 2
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