1. Large language models for G-code generation in CNC machining: A comparison of ChatGPT-3.5 and ChatGPT-4oKristijan Šket, David Potočnik, Miran Brezočnik, Mirko Ficko, Simon Klančnik, 2025, izvirni znanstveni članek Opis: This research explores the viability of producing ISO G-code for 3-axis machining with OpenAI's Chat Generative Pre-Trained Transformer models, particularly ChatGPT-3.5 and the newer GPT-4o. G-code (RS-274-D, ISO 6983) converts human directives into commands that machines can understand, controlling toolpaths, spindle velocities, and feed rates to produce particular aspects of an object. Previously, G-code was generated either by hand or through the use of computer-aided manufacturing (CAM) software along with machine-specific post-processors, both of which may require considerable time and expense. This research aimed to assess the practicality and effectiveness of specific large language models (LLMs) in generating G-code. The assessment took place in three distinct phases on a sample component that required 3-axis machining. These phases included: (1) the self-generated production of G-code for the sample component, (2) the examination of the independently generated G-code in the CAM application, and (3) the recognition and justification of mistakes in the G-code. The outcomes indicated varying abilities with promising findings. This method could accelerate and possibly enhance manufacturing workflows by decreasing reliance on expensive CAM software and specialized knowledge.
Ključne besede: generative artificial intelligence, intelligent manufacturing, large language models (LLM), ChatGPT, CNC machining, G-code programming
Objavljeno v DKUM: 28.11.2025; Ogledov: 0; Prenosov: 7
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2. Ethical approval and informed consent in mental health research : a scoping reviewLeona Cilar Budler, Gregor Štiglic, 2025, pregledni znanstveni članek Opis: Although there is a wide range of scientific papers introducing artificial intelligence techniques in the mental health field, there is a lack of literature assessing the reporting of ethical concerns in such studies. In addition, it is not yet known whether the authors seek ethical approval or informed consent while performing such research. This study aimed to investigate the extent to which studies in the mental health domain that utilize chatbots either ignore or incompletely disclose patient consent and ethical approval from the responsible review boards. A scoping literature search was performed in PsychARTICLES, PubMed, and Web of Science using both MeSH terms and free-text keywords. Following PRISMA-ScR guidelines, we also contacted study authors to verify missing information about ethical approval or informed consent, enhancing the transparency and rigor of our analysis. Among the 27 studies reviewed, 13 reported obtaining ethical approval, and 16 reported collecting informed consent. The remaining studies did not provide such information. These findings underscore the ethical complexities surrounding AI in mental health, especially regarding the collection, storage, and use of sensitive patient data. There is a correlation between sample size and the acquisition of ethical approval, particularly in studies published in journals with low-impact factors. Future research should investigate the role of journal policies in influencing ethical practices. In addition, training programs could be developed to educate researchers on the importance of ethics, particularly in studies with smaller sample sizes.
Ključne besede: artificial intelligence, mental well-being, literature review, ethical issues, patient consent
Objavljeno v DKUM: 18.11.2025; Ogledov: 0; Prenosov: 1
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3. Eexplaining 3D semantic segmentation through generative AI-based counterfactualsDzemail Rozajac, Niko Lukač, Stefan Schweng, Christoph Gollob, Arne Nothdurft, Karl Stampfer, Javier Del Ser, Andreas Holzinger, 2025, izvirni znanstveni članek Opis: Interpreting the predictions of deep learning models on 3D point cloud data is an important challenge for safety-critical domains such as autonomous driving, robotics and geospatial analysis. Existing counterfactual explainability methods often struggle with the sparsity and unordered nature of 3D point clouds. To address this, we introduce a generative framework for counterfactual explanations in 3D semantic segmentation models. Our approach leverages autoencoder-based latent representations, combined with UMAP embeddings and Delaunay triangulation, to construct a graph that enables geodesic path search between semantic classes. Candidate counterfactuals are generated by interpolating latent vectors along these paths and decoding into plausible point clouds, while semantic plausibility is guided by the predictions of a 3D semantic segmentation model. We evaluate the framework on ShapeNet objects, demonstrating that semantically related classes yield realistic counterfactuals with minimal geometric change, whereas unrelated classes expose sharp decision boundaries and reduced plausibility. Quantitative results confirm that the method balances defined interpretability metrics, producing counterfactuals that are both interpretable and geometrically consistent. Overall, our work demonstrates that generative counterfactuals in latent space provide a promising alternative to input-level perturbations.
Ključne besede: 3D point cloud, explainable artificial intelligence, counterfactual analysis, generative AI
Objavljeno v DKUM: 14.11.2025; Ogledov: 0; Prenosov: 6
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4. Use of chatbots in human resource management for more efficient knowledge sharing – systematic literature reviewNejc Bernik, Polona Šprajc, 2025, pregledni znanstveni članek Opis: Purpose: This study examines how chatbots, as part of generative artificial intelligence (GenAI), can assist human resource (HR) professionals in supporting more effective knowledge management (KM), especially knowledge sharing (KS). The research aims to understand the strategic roles of chatbots in Human Resource Management (HRM). It offers propositions for their effective deployment to support KS and enhance their utilisation within organisations. Methodology: A systematic literature review (SLR) was carried out using the databases Web of Science (WoS) and Scopus. After applying inclusion and exclusion criteria, 16 relevant articles were selected for detailed analysis. Results: The findings show that chatbots can significantly enhance KS by automating HRM processes. They enable personalised training, offer continuous support, and promote employee performance, engagement, and innovation. Furthermore, chatbots assist HR professionals in focusing on strategic tasks by lowering administrative workload. Several challenges are also identified, including ethical concerns, privacy issues, data quality problems, reduced social interaction, and risks to creativity and critical thinking. Conclusion: Chatbots offer a transformative opportunity for HRM to enhance KS, organisational memory, and digital learning, thereby supporting competitive advantage in knowledge-intensive settings.
Ključne besede: Chatbots, generative artificial intelligence, human resource management, knowledge management, knowledge sharing
Objavljeno v DKUM: 14.11.2025; Ogledov: 0; Prenosov: 9
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5. Human-led and artificial intelligence-automated critical appraisal of systematic reviews : comparative evaluationLucija Gosak, Gregor Štiglic, Wilson Tam, Dominika Vrbnjak, 2025, izvirni znanstveni članek Opis: Aim To evaluate and compare human-led and artificial intelligence-automated critical appraisal of evidence. Background Critical appraisal is essential in evidence-based practice, yet many nurses lack the skills to perform it. Large language models offer potential support, but their role in critical appraisal remains underexplored. Design We conducted a comparative study to evaluate the performance of five commonly used large language models versus two human reviewers in appraising four systematic reviews on interventions to reduce medication administration errors. Methods We compared large language models and two human reviewers in independently appraising four systematic reviews using the JBI Critical Appraisal Checklist. These models were Perplexity Sonar (Pro), Claude 3.7 Sonnet, Gemini 2.0 Flash, GPT-4.5 and Grok-2. All models received identical full texts and standardized prompts. Responses were analyzed descriptively and agreement was assessed using Cohen’s Kappa. Results Large language models showed full agreement with human reviewers on five of 11 JBI items. Most disagreements occurred in appraising search strategy, inclusion criteria and publication bias. The agreement between human reviewers and large language models ranged from slight to moderate. The highest level of agreement was observed with Claude (κ = 0.732), while the lowest level was observed with Gemini (κ = 0.394). Conclusion Large language models can support aspects of critical appraisal evidence but lack contextual reasoning and methodological insight required for complex judgments. While Claude 3.7 Sonnet aligned most closely with human reviewers, human oversight remains essential. Large language models should serve as adjuncts and not substitutes for evidence-based practice.
Ključne besede: artificial intelligence in healthcare, multimodal large language models, nursing, evidence-based practice
Objavljeno v DKUM: 12.11.2025; Ogledov: 0; Prenosov: 1
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6. 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, izvirni znanstveni članek Opis: 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.
Ključne besede: AI literacy, students, higher education, artificial intelligence
Objavljeno v DKUM: 05.11.2025; Ogledov: 0; Prenosov: 0
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7. Public attitudes towards artificial intelligence and its applicationsTomaž Gjergjek, 2025, magistrsko delo Opis: 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.
Ključne besede: artificial intelligence (AI), public attitudes, perceived threats, societal benefits, trust in institutions, ethical considerations
Objavljeno v DKUM: 30.10.2025; Ogledov: 0; Prenosov: 9
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8. 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, pregledni znanstveni članek Opis: 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.
Ključne besede: advance care planning, digital tools, palliative care, artificial intelligence, machine learning
Objavljeno v DKUM: 29.10.2025; Ogledov: 0; Prenosov: 5
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9. Machine learning in antiviral drug designAnja Kolarič, Marko Jukič, Urban Bren, 2026, pregledni znanstveni članek Opis: 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.
Ključne besede: machine learning, artificial intelligence, antiviral compounds, biological activity
Objavljeno v DKUM: 17.10.2025; Ogledov: 0; Prenosov: 4
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10. 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, pregledni znanstveni članek Opis: 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.
Ključne besede: artificial intelligence, machine learning, agriculture, soil health, soil parameter modeling, regional data bias
Objavljeno v DKUM: 17.10.2025; Ogledov: 0; Prenosov: 4
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