Gacha games are the most dominant games on the mobile market. These are free-to-play games with a lottery-like system, where the user pays with in-game currency to enter a draw in order to obtain the character or item they want. If a player does not obtain what he hoped for, there is the option of paying with his own money for more draws, and this is the main way to monetize the Gacha game. The purpose of this study is to show the playing and spending habits of Gacha players: the reasons they like such games, the reasons for spending, how much they spend, what they spend on, how long they have been spending, and whether they are aware of their spending. The paper includes studies by other researchers on various aspects of Gacha games as well. The aim of the paper is to conduct a study with the hypothesis that players who play the same game for a while and have a habit of playing it are willing to give more of their money to enter a draw. Therefore, two research questions and two hypotheses were analyzed. A total of 713 participants took part in the study.

Programming education traditionally requires extensive manual assessment of student assignments, which is both time-consuming and resource-intensive for instructors. Recent advances in large language models (LLMs) open opportunities for automating this process and providing timely feedback. This paper investigates the application of artificial intelligence (AI) tools for preliminary assessment of undergraduate programming assignments. A multi-phase experimental study was conducted across three computer science courses: Introduction to Programming, Programming 2, and Advanced Programming Concepts. A total of 315 Python assignments were collected from the Moodle learning management system, with 100 randomly selected submissions analyzed in detail. AI evaluation was performed using ChatGPT-4 (GPT-4-turbo), Claude 3, and Gemini 1.5 Pro models, employing structured prompts aligned with a predefined rubric that assessed functionality, code structure, documentation, and efficiency. Quantitative results demonstrate high correlation between AI-generated scores and instructor evaluations, with ChatGPT-4 achieving the highest consistency (Pearson coefficient 0.91) and the lowest average absolute deviation (0.68 points). Qualitative analysis highlights AI’s ability to provide structured, actionable feedback, though variability across models was observed. The study identifies benefits such as faster evaluation and enhanced feedback quality, alongside challenges including model limitations, potential biases, and the need for human oversight. Recommendations emphasize hybrid evaluation approaches combining AI automation with instructor supervision, ethical guidelines, and integration of AI tools into learning management systems. The findings indicate that AI-assisted grading can improve efficiency and pedagogical outcomes while maintaining academic integrity.

The paper presents a case study on using artificial intelligence (AI) for preliminary grading of student programming assignments. By integrating our previously introduced learning programming interface Verificator with the Gemini 2.5 large language model via Google AI Studio, C++ student submissions were evaluated automatically and compared with teacher-assigned grades. The results showed moderate to high correlation, although the AI was stricter. The study demonstrates that AI tools can improve grading speed and consistency while highlighting the need for human oversight due to limitations in interpreting non-standard solutions. It also emphasizes ethical considerations such as transparency, bias, and data privacy in educational AI use. A hybrid grading model combining AI efficiency and human judgment is recommended.

One of the main challenges in aerial photogrammetry is the lack of standardization in the process of data integration, which is the first step in 3D reconstruction. This study analyses the influence of the distance from the camera to the subject of the recording, the influence of different overlap percentages in photography as well as the difference between the three methods of re-cording in programming flight plans. DJI Phantom 3 unmanned aerial vehicle and PIX4Dmapper software for 3D reconstruction were used in this research. The old town of Medvedgrad near Zagreb, Croatia was the subject of recording. The purpose of this study is to design a standardized image recording protocol that would contribute to a faster and more optimal workflow while maintaining a quantitatively measured quality according to the total number of 2D key points per image and the total number of 3D key points in a point cloud. Based on research and the obtained results, the possibility of data recording at a distance of up to 30 m without a significant loss (3.6%) of the number of 2D key points per photograph was proven. A larger horizontal distance allows recording from one viewing altitude if the height of the subject does not exceed 30 m. This study determines that 85% image overlap results in an almost identical number of 3D key points (0.7% difference) as well as 90% image overlap. The authors proposed a new flight plan, which would implement optimized parameters obtained in this research.