The development of artificial intelligence contributes to the digital transformation of the entire society, improving numerous processes, automation and better decision-making. In addition to the many opportunities it offers, it also brings with it great risks in data security. The problem is further complicated by unauthorized access to data processed by artificial intelligence models, data theft, ethical dilemmas and lack of algorithm transparency. This paper analyzes artificial intelligence through different fields and the problems it can cause. A special emphasis is on the research into user attitudes, which shows us what kind of knowledge respondents have about artificial intelligence and all the risks it can cause. The results help us to see what the biggest problems regarding the use of such models are. The work provides guidelines for minimizing risks and creating problems, while dictating the trend for responsible use of artificial intelligence for legally correct purposes.

Modern distance learning platforms represent important infrastructure in contemporary higher education, particularly during periods of intensive use such as examinations, assignment deadlines, and simultaneous access to learning materials. In such situations, Learning Management System (LMS) platforms may face sudden traffic spikes that can lead to increased latency, reduced availability, and service degradation. Traditional autoscaling mechanisms in Kubernetes commonly rely on CPU or memory utilization, which may react too late when overload first appears at the network or application layer. This paper proposes a simulation-based latency-aware autoscaling model for LMS platforms in Kubernetes-like cloud-native environments. The model uses network latency measured at the ingress layer as an early control signal for adaptive horizontal scaling. The proposed architecture conceptually integrates the NGINX Ingress Controller, Prometheus-based telemetry, a Custom Metrics Adapter, and the Horizontal Pod Autoscaler within a closed feedback loop based on the MAPE-K paradigm. The model was evaluated through a Python-based simulation that replicates bursty load conditions in an LMS environment, supporting up to 2000 concurrent users or requests per second. The simulation results indicate that the latency-aware approach can initiate scaling earlier than a traditional CPU-based approach under the defined workload assumptions. In the simulated environment, the latency-aware model reduced the time to the first scaling action from approximately 90 s in the CPU-based baseline to approximately 12–15 s under the same workload assumptions. This result should not be interpreted as a direct reduction in application response time, but as an earlier activation of the scaling mechanism in the simulation. Since the validation was carried out in a simulated environment, rather than in a real Kubernetes cluster, these results should be interpreted within the limits of the simulation assumptions. In future research, the proposed model can be implemented in a real Kubernetes cluster using NGINX Ingress, Prometheus, HPA, and load generation tools such as Locust, JMeter, or k6.

Smart contracts are one of the most prevalent and important blockchain-based technologies in the field of financial security, as the automatic execution of predefined rules provides additional efficiency, lowers costs and minimizes the involvement of intermediaries. This research will examine the use of smart contracts for process automation, risk reduction and organizational restructuring in the financial sector. In particular, the interaction between centralized and decentralized financial systems, the technology behind the implementation of blockchain and security issues associated with the use of smart contracts will be considered. At the same time, escrows will be presented as an example of the practical use of smart contracts for financial operations. The results of this analysis will show that smart contracts can be used as a means to increase the reliability of financial operations; however, their widespread use depends on proper regulation, security assessment and integration with the existing financial infrastructure.

The increasing integration of artificial intelligence (AI) in higher education highlights the need for valid instruments to assess students’ perceptions of its use in e-learning environments. The aim of this study was to develop and validate a psychometric scale for measuring students’ perceptions of artificial intelligence in e-learning. A scale development research design was employed on a sample of 225 higher education students. The development process included four phases: item generation based on the technology acceptance model (TAM) and relevant literature, content validation through expert evaluation, pilot testing, and empirical validation. Exploratory factor analysis (EFA) identified a stable three-factor structure consisting of perceived usefulness, efficiency and optimization, and support in teaching. The Kaiser-Meyer-Olkin value (.812) and Bartlett’s test of sphericity (p < .001) confirmed data adequacy. The scale demonstrated satisfactory reliability, with Cronbach’s alpha coefficients of .889, .823, and .767. Discriminant validity was confirmed using an independent samples t-test, while inter-factor correlations supported construct validity. The findings indicate that the developed instrument is a reliable and valid tool for assessing students’ perceptions of artificial intelligence in e-learning environments.Keywords: artificial intelligence; e-learning; scale development; psychometric validation; factor analysis; student attitudes --- SažetakSve veća integracija umjetne inteligencije (UI) u visokom obrazovanju naglašava potrebu za valjanim instrumentima za procjenu percepcija studenata o njezinoj primjeni u e-učenju. Cilj je ovoga istraživanja razviti i validirati psihometrijsku skalu za mjerenje percepcija studenata o umjetnoj inteligenciji u e-učenju. Primijenjen je istraživački dizajn razvoja skale na uzorku od 225 studenata visokoga obrazovanja. Proces razvoja obuhvatio je četiri faze: generiranje čestica na temelju modela prihvaćanja tehnologije (TAM) i relevantne literature, validaciju sadržaja putem ekspertne evaluacije, pokusno testiranje te empirijsku validaciju. Eksploratornom faktorskom analizom (EFA) identificirana je stabilna trofaktorska struktura koja uključuje percipiranu korisnost, učinkovitost i optimizaciju te podršku u nastavi. Vrijednost Kaiser-Meyer-Olkinova pokazatelja iznosila je ,812, a Bartlettov test sferičnosti bio je statistički značajan (p < ,001), što potvrđuje prikladnost podataka. Skala je pokazala zadovoljavajuću pouzdanost, s Cronbachovim alfa koeficijentima od ,889, ,823 i ,767. Diskriminantna valjanost potvrđena je primjenom t-testa za nezavisne uzorke, dok su korelacije među faktorima podržale konstrukt valjanosti. Rezultati ukazuju na to da razvijeni instrument predstavlja pouzdan i valjan alat za procjenu percepcija studenata o umjetnoj inteligenciji u e-učenju.Ključne riječi: e-učenje; faktorska analiza; psihometrijska validacija; razvoj skale; stavovi studenata; umjetna inteligencija.

In the modern higher education environment of the field of Information Technology (IT), the application of modern technological tools is more and more oriented toward the integration of the existing gap between theoretical and applied knowledge. One of the major problems in the development of IT courses is the process of introductory programming, often associated with high levels of cognitive load and varied levels of student performance. This paper aims to investigate the pedagogical potential and operational application of the integration of AI-based tools of virtual agents in the process of programming education. This is based on the theoretical background of the constructivist approach and the self-regulated learning (SRL) model. The empirical part of the research, carried out among IT students, examines their views concerning the usefulness of virtual assistants in overcoming syntax barriers and improving problem-solving fluency. The research findings suggest that although AI tools greatly alleviate cognitive load and motivate learners, it is necessary to apply a didactic approach in order to develop genuine computational thinking. The research findings contribute to the discussion aimed at improving the quality of IT study programs and making them compatible with the changing needs of the global labor market.