This paper explores the benefits of using Virtual Reality (VR) technologies in higher education. The theoretical part investigates the classical education system and its features in order to compare advantages of using VR systems in education. VR technologies and its current state in industry and in education were explored in addition to which branches of higher education use these systems. A survey was conducted through an online questionnaire where respondents (N = 55) gave their opinion on VR and the implementation of VR technologies in education. Three hypotheses related to the use of VR technology, student interest, and learning outcomes as well as the effectiveness, immersiveness and the effect of VR systems on the users were tested through 27 questions.

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 examines literature on the subject of instructional design and game based learning with the aim of identifying its positive effects and impact on users. Special attention was given to learning, encouragement and development of...

Game cheating is a common occurrence that may degrade the experience of “honest” players. It can be hindered by using appropriate anticheat systems, which are being considered as a subset of security-related issues. In this paper, we implement and test an anticheat system whose main goal is to help differentiate human players from AI players. For this purpose, we first developed a multiplayer game inside game engine Unity that would serve as a framework for training the reinforcement learning agent. This agent would thus learn to differentiate human players from bots within the game. We implemented the Machine Learning Agents Toolkit library, which uses the proximal policy optimization algorithm. AI players are implemented using state machines, and perform certain actions depending on which condition is satisfied. Two experiments were carried out for testing the agent and showed promising results for identifying artificial players.

This paper addresses the issue of reconstructing digital paintings and making them visible with the use of an augmented reality application. It contributes through raising awareness of stolen heritage due to a necessity to ensure digital...