International Scientific Journals
of Scientific Technical Union of Mechanical Engineering "Industry 4.0"

Artificial Intelligence (AI) is rapidly transforming Learning and Development (L&D) by enabling personalized learning experiences, accelerating content creation, improving multilingual delivery, and supporting data-driven decision-making. While current adoption predominantly focuses on content production efficiency, the strategic use of AI for workforce development and skills management remains underdeveloped. This paper examines the role of AI-based training solutions in corporate learning ecosystems, drawing on recent industry research and insights from qualitative interviews with three learning and development experts. The study discusses opportunities, implementation challenges, ethical considerations, and the emerging shift toward AI-native learning platforms. Recommendations for future implementation and research are provided.

The rapid advancement of biotechnology, artificial intelligence, big data analytics, and digital technologies is transforming the nature of biological risks and creating new challenges for security systems. At the same time, the processes of bioconvergence are fostering increasingly close interactions among the life sciences, engineering, information technologies, and security studies. This paper explores the implications of these developments for the education and training of biological security specialists. Drawing on a systems approach, comparative analysis, and conceptual modelling, the analysis identifies the key competencies required for effective professional performance in a dynamic technological environment. An original competency model is proposed, integrating fundamental biological knowledge, technological competencies, security-related expertise, and practical skills for biological risk management. The model provides a conceptual framework for the modernization of educational programmes and for strengthening sustainable capacity to address biological threats.

This paper investigates the efficacy of generative artificial intelligence in automating the creation of technical documentation for non-destructive testing (NDT). The research focuses on a comparative performance analysis between a specialized AI agent, built on the Gemini 3.1 Flash platform, and certified human NDT Level 2 experts. A comparative cross-sectional study was conducted using three distinct industrial forging products. Both the AI agent and two human experts were tasked with generating Visual Testing (VT) work instructions based on a 14-point framework derived from the STN EN 13018 and ISO 9712 standards. The outputs were evaluated by a blind-reviewing Level 3 expert using a modified HEAT (Expertise, Accuracy, Trust) rubric, focusing on regulatory compliance, technical precision, and readability. he results demonstrate that the AI agent achieved a 98.4% reduction in generation time, averaging 54.5 seconds per instruction compared to 57.3 minutes for human experts. While the AI agent consistently outperformed humans in text clarity and structural consistency (scoring 5.0 in usability), it exhibited “conservative technical hallucinations,” such as prescribing unnecessary magnification tools and incorrect defect-coding standards (ISO 6520-1 instead of CSN 421240). Furthermore, cloud-based API instabilities (HTTP 503 errors) were identified as a critical reliability risk for real-time industrial deployment. The study concludes that while AI agents are highly effective as rapid drafting tools, they cannot currently replace certified personnel due to lack of situational engineering judgment and legal accountability. A “Human-in-the-loop” model remains mandatory, where a certified Level 2 or 3 professional must verify and approve all AI-generated NDT documentation to ensure industrial safety and regulatory compliance…

Development of artificial intelligence in recent years has opened new possibilities in numerical analysis and engineering design. This paper systemizes the main area of applications of artificial intelligence in engineering with emphasis on computational mechanics. Fundamental approaches of machine learning are described including artificial neural networks, convolutional neural networks (CNN) and physics-informed neural networks and their integration with the finite element method. Additionally, a review is given on the platform ANSYS SimAI platform which combines predictive accuracy of numerical simulations with the speed of generative artificial intelligence in a cloud or local computer environment reducing computational time by one or two orders of magnitude. It is worth noting that artificial intelligence is not a replacement for classical analysis, but a powerful tool which speeds up the innovation process and can expand boundaries of numerical modeling in engineering

Bankruptcy procedures are complex and often slow, requiring extensive documentation, strict deadlines, multiple creditors, significant human resources, and numerous judicial decisions In Albania, these procedures remain predominantly manual; therefore, the use of Artificial Intelligence opens new opportunities to enhance efficiency and transparency in these procedures. The main benefits include reducing the duration and improving the effectiveness of actions, increasing the accuracy and reliability of the process, and generating useful statistics for the formulation of public policies. Nevertheless, challenges remain related to the adoption of an appropriate legal framework, the protection of sensitive data, the transparency of algorithms, and the determination of legal responsibility. AI has the potential to transform bankruptcy procedures in Albania, but it requires a cautious approach that balances technological innovation with the protection of the legal rights of the parties involved. In this regard, harmonization of legislation with European Union standards and its proper implementation represent the most critical issues in this domain.

Pre-university education in general is facing various challenges, but in the schools of the Municipality of Kamenica the challenges are even greater in improving learning outcomes in teaching and learning, these challenges range from identifying individual student needs and optimizing learning resources.
This paper examines the role and impact of artificial intelligence (AI) in improving and facilitating learning circumstances in schools in the Municipality of Kamenica in Kosovo. The main goal is to analyze how AI solutions can improve the level of learning, support effective teaching and help in assessing and attracting students by increasing their level of concentration.
In this paper, we have taken an empirical approach using real data from several pre-university education schools in Kamenica, which include test scores, attendance statistics, and information on learning activities. With the help of machine learning techniques, models have been built that identify student profiles with different performance and recommend personalized learning strategies for each profile. An AIbased recommender system has also been developed that suggests teaching materials and relevant exercises using virtual laboratories according to the needs and individual progress of students.
The results show that the use of AI tools helps in identifying student weaknesses more quickly, in creating personalized lesson plans and in facilitating the work of teachers for continuous monitoring and evaluation. The analysis also shows the perception of teachers and parents across the Municipality of Kamenica towards the integration of AI in teaching practice, highlighting the challenges and opportunities for wider implementation in pre-university education in Kosovo.
This case study that we have conducted in the Municipality of Kamenica offers a practical and strategic contribution to municipal education policies towards the effective use of advanced technologies in the teaching process not only in the Municipality of Kamenica but also beyond.

Nonlinear dynamical systems arise in numerous scientific and engineering domains, including physics, economics, biology, and control theory. Their complex behavior, sensitivity to initial conditions, and possible chaotic dynamics make accurate modeling and prediction challenging using traditional analytical approaches alone. In recent years, artificial intelligence (AI) techniques have demonstrated strong potential for modeling nonlinear and complex systems through data-driven methods. This paper explores artificial intelligence approaches for modeling nonlinear dynamical systems, focusing on the integration of machine learning techniques with classical mathematical modeling. We consider representative nonlinear systems and analyze how neural networks, regression models, and hybrid AI–mathematical frameworks can be used to approximate system behavior, predict future states, and capture hidden structures in time-series data. Special attention is given to systems exhibiting chaotic behavior, where small perturbations in initial conditions can lead to significant divergence in trajectories. The study presents numerical simulations and comparative analyses between traditional mathematical models and AI-based approaches. The results highlight the advantages of machine learning methods in capturing nonlinear patterns and improving predictive accuracy, especially when analytical solutions are difficult or unavailable. Additionally, we discuss the interpretability of AI models in the context of dynamical systems and outline potential applications in engineering, intelligent control, and data-driven system identification. The proposed framework contributes to the growing intersection between dynamical systems theory and artificial intelligence by demonstrating how AI tools can support the analysis and modeling of complex nonlinear phenomena. This work aims to provide a foundation for future research on hybrid mathematical–AI methods for understanding and predicting complex systems.

In this work, AI is examined as a tool for the simulation of climate scenarios and the systemic modelling of the future of the built environment under conditions of climate change. Rather than relying on numerical physical simulation, the approach is based on qualitative analysis using a set of possible future scenarios, in which the same building is considered within different climatic and economic contexts up to the end of the twenty-first century. The study examines how deteriorating climatic conditions redirect resources from development toward the compensation of losses and how this transforms the architectural environment over time. The paper discusses the potential for buildings to act as active participants in the carbon balance through mechanisms for CO₂ removal and long-term fixation. AI supports the analysis of causal relationships between climate scenarios, economic incentives, and architectural decisions in order to avoid systemically problematic strategies. The approach is applicable both in professional practice and as an educational exercise for architecture students.

The authentication of fine art has customarily relied on expert connoisseurship, material analysis, and provenance research. In recent years, artificial intelligence (AI) and AI-based technologies have appeared as significant tools in this domain, enabling new forms of algorithmic evidence, probabilistic reasoning, and large-scale pattern recognition. This paper examines how AI-based systems support museums, galleries, collectors, and private institutions in authenticating fine art paintings. It argues that AI does not replace human expertise but establishes a hybrid epistemic framework in which algorithmic forensics and art-historical knowledge co-produce authenticity. The study analyses key technological approaches, institutional applications, epistemological implications, and structural drawbacks, positioning AI as a catalytic agent in reconfiguring trust, authority, and knowledge production within the contemporary art ecosystem.

This study presents the development of an AI-assisted digital guide designed to support cultural and religious tourism through the integration of interactive mapping and text-to-speech technologies. The project focuses on the cultural heritage of the city of Svishtov, Bulgaria, where several historically significant temples and monasteries are organized into a structured digital cultural route. Historical and descriptive information about each site was collected from local historiography and field observations and subsequently transformed into a digital format suitable for web presentation. An interactive map created in Google Maps allows users to visualize the spatial distribution of the sites and navigate the cultural route. In addition, an AI-generated audio guide based on text-to-speech technologies provides accessible audio narration for each location, enhancing the interpretive experience for visitors. The platform integrates visual content, geographic navigation, and automated voice narration within a single digital environment. The proposed approach demonstrates how artificial intelligence tools and interactive mapping technologies can support the interpretation of cultural heritage, improve the accessibility of historical information, and create engaging digital experiences for visitors. The study highlights the potential of combining digital humanities approaches with modern AI technologies for the promotion of local cultural heritage and the development of innovative tourism applications.

Artificial intelligence (AI) is reshaping the field of penetration testing by enabling faster, more adaptive simulations of cyber threats. This paper explores how AI can be ethically integrated into penetration testing processes, focusing on the European Defence Fundbacked TRITON project. TRITON proposes a comprehensive AI-driven framework for testing the security of military and critical infrastructure systems, combining technologies like machine learning, generative models, and reinforcement learning. The TRITON project reviews recent advancements in AI-supported pentesting and discusses how these tools can improve vulnerability detection, attack simulations, and threat modeling. Alongside the technical discussion, which is examined in the TRITON project, ethical concerns—including transparency, human oversight, and dual-use risks—are of the essence and must be addressed to ensure responsible use. Comparisons with other EU cybersecurity initiatives, such as AI4CYBER and CyberSecDome, highlight TRITON’s unique contributions and focus areas. Ultimately, it argues that AI-enhanced penetration testing can significantly strengthen cybersecurity defenses when implemented with appropriate safeguards and ethical oversight.

The present study examines autonomous factories as a contemporary manifestation of digital transformation in the manufacturing sector. Unlike traditional automation, autonomous systems demonstrate cognitive capabilities through the integration of artificial intelligence, machine learning, and cyber-physical systems. The technological architecture includes the Internet of Things, digital twins, advanced robotics, and blockchain technologies, functioning in synergy. The implementation process follows a phased evolution through five maturity levels. The analysis reveals multiple challenges – technical, financial, ethical, and regulatory. Empirical data shows that the primary difficulties stem from psychological resistance and organizational adaptation, rather than from technological limitations. The research examines the economic effects, changes in employment structure, and the social dimensions of the transformation. In conclusion, the necessity for a balanced approach is emphasized, integrating technological capabilities with human needs in the context of Industry 5.0 philosophy.