Artificial Intelligence (AI) is becoming an important part of modern educational reforms, introducing innovative approaches and learning methods [2]. This study explores the application of artificial intelligence in the education system, examining whether a tool such as ChatGPT can generate pedagogically relevant and curriculum-aligned teaching materials. The research methodology is based on the analysis of the role of AI in education, focusing on the evaluation of the quality, accuracy, and pedagogical value of the content generated by ChatGPT-5. The study combines international research on the use of generative AI in schools with an analysis of materials created for teaching biology and mathematics in the sixth grade of primary school. The analysis included simple, detailed, and curriculum-aligned prompts to examine how different prompt types affect cognitive complexity, language clarity, and alignment with learning outcomes. The results show that all generated materials were factually accurate but differed in educational value. Tasks created using detailed and curriculum-aligned prompts demonstrated higher pedagogical relevance and contributed to deeper understanding and the development of critical thinking skills among students. The research confirms that thoughtful and responsible use of artificial intelligence can provide significant support to teachers in creating quality and educationally meaningful teaching materials.

This study investigates flight route optimization in a large airline network by comparing traditional path-finding algorithms with an evolutionary approach under realistic constraints. Using a real-world dataset of 6,817 routes, Dijkstra’s algorithm, the A ${}^{\ast }$ search algorithm with precomputed heuristics, and a Genetic Algorithm (GA) were implemented. All methods were evaluated on identical origin–destination pairs while enforcing an exact number of layovers, and on scenarios where direct flights were removed or newly introduced. The results show that, although Dijkstra’s algorithm always returns the optimal shortest-time route when one exists, its computation time increases rapidly as the number of required layovers increases. A ${}^{\ast }$ improved the efficiency in dense subgraphs but sometimes failed under strict layover constraints owing to heuristic misalignment. In contrast, GA consistently produced near-optimal routes with stable runtimes across varying connectivity levels and constraint settings. Route removal experiments highlighted the vulnerability of sparsely connected regions, whereas adding direct flights mainly benefited airport pairs previously linked only by long multi-stop paths. These findings indicate that evolutionary algorithms are well-suited for large-scale, dynamically changing airline networks, whereas classical algorithms remain preferable for smaller, well-structured subproblems.

The growing use of DGs presents challenges for system planners and operators, demanding strategic adaptations to accommodate diverse energy sources while ensuring grid stability and operational efficiency. HC analysis has recently been proposed as an essential tool capable of guiding investments into the areas of the network, most likely to offer optimal benefits. This paper presents a method for estimation of photovoltaic HC of the distribution network. For this purpose, the OpenDSS program, employing the Monte Carlo-based method, is utilized to quantify the HC of the electrical distribution network. The simulation is conducted using a real electrical MV network and then verified against the IEEE Test System for validation. This research reports higher HC in comparison with similar methods and models, investigates the influence of constant generation in daily simulation and proves that voltage constraint is violated before line loading. A considerable increase of the circuit losses is recorded if the optimal penetration of PV is exceeded. The contribution of this work is development, testing and implementation of HC estimation method in complex power systems using open-source tools and integrating them in innovative fashion. The results of this research contribute to collective endeavours of energy transition and sustainability.

A microgrid concept for the thermal and electrical energy supply of a Sport-Recreation Center Ajdinovići (S.R.C.A.) has been proposed in this paper. A self-contained and intelligent power distribution grid has been developed for this case, taking into account the location, locally available renewable energy sources and the very purpose of this center. Comparative analyses between independent power supply through the proposed hybrid power system and the supply over the transmission and distribution network were performed. Technical and economical optimization of an energy system with distributed power generation was done by applying HOMER and DIgSILENT PowerFactory professional software tools. As a result, hybrid power system is more cost-effective than the conventional supply by the power distribution network, microgrid meets the technical criteria based on the analysis of power flows and the stability of the network has been achieved. Establishment of energy independence for the existing S.R.C.A. besides economic factor will show several benefits regarding better use of locally available resources, reduction of CO2 emissions and energy efficiency increment as well as an employment opportunity to the local people, thus contributing to the sustainability of the region.