## 6 Discussion and Conclusions
Overall, the choice of maze generation algorithm should be guided by the application's requirements, balancing factors such as generation speed, path complexity, and navigational characteristics to enhance the learning experience and maintain user engagement. However, this study has several limitations. First, the comparative analysis of maze generation algorithms was conducted using a specific set of parameters and on a single type of mobile device. Variations in device capabilities and user interfaces could impact the algorithms' performance. Future research should consider a broader range of devices and user interfaces to validate the generalizability of these findings. While the study focused on the technical performance of maze generation algorithms, it did not extensively evaluate the pedagogical effectiveness of the generated mazes in enhancing programming skills and problem-solving abilities. The study lacks a comprehensive user-centered evaluation to assess how different maze complexities generated by these algorithms impact the actual learning experience of children. Factors such as user engagement, motivation, and learning outcomes were not directly measured, which limits the understanding of how well these algorithms serve the intended educational purpose. Future research should address these limitations by testing a broader range of devices, adjusting algorithm parameters for diverse applications, and incorporating educational impact studies to validate and enhance the effectiveness of maze-based learning tools. Expanding the research to include a broader range of devices and operating systems would help generalize the findings and ensure that the educational tools developed are accessible and effective for a wide audience. This could involve testing the algorithms on devices with varying specifications and exploring how different input methods (e.g., touch, voice, and gestures) affect the learning experience. Another important area for future research is the development of new algorithms or the modification of existing ones to create more tailored educational experiences. For instance, algorithms could be designed to generate mazes that gradually introduce new programming concepts or that adapt in real time to the learner's performance, providing a personalized learning path. Furthermore, the study suggests that the impact of different user interfaces and interaction methods on the effectiveness of maze-based learning tools should be examined. With the rise in new technologies such as augmented reality and virtual reality, future research could investigate how these technologies can be integrated with maze generation algorithms to create immersive and engaging learning environments. These future research directions could significantly enhance the understanding and effectiveness of maze-based educational tools, ultimately contributing to better educational outcomes in the domain of computational thinking and programming for young learners. Conceptualization, M.C., O.S. and I.D. ; methodology, M.C. and O.S. ; software, M.C. ; validation, O.S., I.D. and D.C.; formal analysis, M.C. and O.S. ; investigation, M.C. ; resources, I.D. and D.C.; data curation, M.C. and I.D. ; writing--original draft preparation, M.C. and O.S. ; writing--review and editing, M.C., O.S., I.D. and D.C.; visualization, M.C., O.S. and I.D. ; supervision, M.C. ; project administration, M.C. ; funding acquisition, I.D. and D.C. All authors have read and agreed to the published version of the manuscript. This research was funded by the project \"Application of VR technology and neural networks in the field of computer security and digital forensics\" grant number NPOO2024-1. The project is financed by the Ministry of Science, Education, and Youth through EU funding (Recovery and Resilience Facility). The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding authors. The authors declare no conflict of interest.