This paper applies a hybrid multi-criteria decision-making (MCDM) model that integrates the Analytic Hierarchy Process (AHP) for structured weighting of evaluation criteria with the Net Worth Analysis (NWA) method for value-based aggregation of scores. The proposed framework was employed to evaluate Learning Management Systems (LMS) in higher education, involving two independent expert panels representing management and IT perspectives. Results of the AHP analysis show that cost (28%), security (22%), and usability (17%) are the most influential criteria in the decision-making process, reflecting institutional priorities for financial efficiency, safety and ease of use. Based on the combined AHP-NWA model, Moodle 4.3 emerged as the most sustainable choice (0.586), followed by Atutor 2.2.1 (0.541) and Blackboard (SaaS edition) (0.490). The inclusion of sensitivity and scenario analyses confirmed the robustness of the model, demonstrating that the ranking of alternatives remains stable under variations in weighting factors and different strategic priorities. By framing LMS evaluation within the context of sustainable digital transformation, the study emphasizes how transparent and systematic decision-making supports long-term institutional resilience and aligns with the principles of Education for Sustainable Development (ESD). In addition, the framework contributes to the achievement of Sustainable Development Goal 4 (Quality Education), by guiding higher education institutions toward inclusive, resilient and cost-effective digital solutions.

This paper presents a comprehensive model for cyber security risk assessment using the PIPRECIA-S method within decision theory, which enables organizations to systematically identify, assess and prioritize key cyber threats. The study focuses on the evaluation of malware, ransomware, phishing and DDoS attacks, using criteria such as severity of impact, financial losses, ease of detection and prevention, impact on reputation and system recovery. This approach facilitates decision making, as it enables the flexible adaptation of the risk assessment to the specific needs of an organization. The PIPRECIA-S model has proven to be useful for identifying the most critical threats, with a special emphasis on ransomware and DDoS attacks, which represent the most significant risks to businesses. This model provides a framework for making informed and strategic decisions to reduce risk and strengthen cyber security, which are critical in a digital environment where threats become more and more sophisticated.

This paper provides a bibliometric analysis of current research trends in the field of artificial intelligence (AI), focusing on key topics such as deep learning, machine learning, and security in AI. Through the lens of bibliometric analysis, we explore publications published from 2020 to 2024, using primary data from the Clarivate Analytics Web of Science Core Collection. The analysis includes the distribution of studies by year, the number of studies and citation rankings in journals, and the identification of leading countries, institutions, and authors in the field of AI research. Additionally, we investigate the distribution of studies by Web of Science categories, authors, affiliations, publication years, countries/regions, publishers, research areas, and citations per year. Key findings indicate a continued growth of interest in topics such as deep learning, machine learning, and security in AI over the past few years. We also identify leading countries and institutions active in researching this area. Awareness of data security is essential for the responsible application of AI technologies. Robust security frameworks are important to mitigate risks associated with AI integration into critical infrastructure such as healthcare and finance. Ensuring the integrity and confidentiality of data managed by AI systems is not only a technical challenge but also a societal necessity, demanding interdisciplinary collaboration and policy development. This analysis provides a deeper understanding of the current state of research in the field of AI and identifies key areas for further research and innovation. Furthermore, these findings may be valuable to practitioners and decision-makers seeking to understand current trends and innovations in AI to enhance their business processes and practices.

Artificial intelligence (AI) is becoming increasingly important in higher education, which has resulted in the accelerated development of research in this area. This paper conducts a bibliometric analysis of scientific papers researching AI applications in higher education, using the Web of Science database. The analysis covers the period from 1996 to February 2024 and focuses on the most cited works in this field, a total of 82 papers, with 1011 citations (944 without self-citations). Our analysis shows that interest in AI has increased significantly over the past few years, with the most dominant research in the fields of education, computer science, and engineering. The largest number of papers was published in 2023, which indicates the growing importance of this topic. These results provide a foundation for future research on the impact of AI on educational practices, its challenges, and its potential to transform education in the future.

Artificial intelligence and ChatGPT became very popular in late November 2022. Their popularity has led to the development of other chat AI systems and AI tools. In order to monitor this progress and achieve key goals in the application of artificial intelligence, this paper has the task of presenting an analysis of the generated web mining program. One of the key aspects of this research is the presentation of advantages and disadvantages of this approach, including analysis of accuracy, speed and reliability. Also, potential challenges in the application of artificial intelligence (especially chat systems), such as the limitations of AI models, ethical and security aspects when processing data from the Internet, are explored. This research paper aims to contribute to a better understanding of the capabilities of AI in the development of web mining programs, identify potential areas of improvement and provide guidance for future development.

This study applies the PIPRECIA-S model to evaluate and prioritize artificial intelligence (AI) applications in higher education. The aim is to identify which AI-driven solutions offer the greatest potential for improving teaching, learning and institutional management. Seven relevant criteria were defined based on the literature and expert consultation: pedagogical effectiveness, student engagement, adaptability, ease of implementation, data security, cost efficiency and long-term sustainability. Four AI applications were selected for evaluation: adaptive learning systems, intelligent tutoring systems, predictive analytics for student success and automated administrative tools. A panel of five domain experts independently provided pairwise comparisons to weight the criteria, while a separate panel assessed the alternatives. The results indicate that Adaptive Learning Systems achieved the highest overall score (4.375), followed by Intelligent Tutoring Systems (4.107) and Predictive Analytics (4.062), while Administrative Tools (3.647) ranked lowest. These findings highlight the pedagogical priority of AI solutions that directly enhance learning outcomes and student retention. The paper contributes by demonstrating how multi-criteria decision-making (MCDM) models, such as PIPRECIA-S, can support evidence-based policy and strategic planning in higher education, providing both theoretical insights and practical guidelines for sustainable AI integration.