## 7 Limitations and Future Works  
Although our research findings are promising, we acknowledge certain limitations in our study that require further exploration in future research. The data to train and validate the deep learning model was limited to one game. Given the diversity of gameplay mechanics, styles, and player interactions across various games, it is not possible to acknowledge the model's generalizability. Furthermore, a player's skill level may not match their preferred game difficulty. For instance, highly skilled players might opt for an easier gaming experience, whereas less-skilled players may seek challenges beyond their current abilities. Future research could explore mechanisms that account for player preferences when adjusting game difficulty to achieve a more personalized gaming experience. In addition, our current system adjusts the difficulty level based on skill classification only once a game session is concluded. This method must be revised to adjust for real-time player skills and engagement fluctuations during a gaming session. We must consider the computational complexity of our deep learning model, which may impose constraints on its practical implementation, particularly in environments with limited resources. Future research could leverage more computationally efficient models or optimization techniques to address complexity. The goal is to reduce the computational load while ensuring that the model's accuracy in classifying player skill and adjusting game difficulty remains uncompromised. The study did not present an evaluation with a control group in which the DL model is not utilized during gameplay. By comparing the standard gameplay of Astro with the gameplay featuring complexity adjustment, it would be possible to determine whether there were statistically significant differences in the gaming experience between the two conditions. Moreover, there is a need to delve deeper into players' perceptions of the complexity adjustment mechanism. Understanding how players perceive and react to dynamic difficulty adjustments can provide valuable insight into their subjective experiences and emotional responses. This exploration can shed light on the factors contributing to player motivation, self-evaluation, and overall enjoyment of the gaming experience. Additionally, an analysis to explore potential gender differences in reactions and responses would allow for developing more inclusive and tailored approaches to game design and difficulty adjustment. Our goal is to enhance the effectiveness and practicality of dynamic game difficulty adjustment systems by recognizing our limitations and developing strategies to overcome them. By doing so, we aim to improve the overall gaming experience and increase player engagement and satisfaction in the industry. Conceptualization, P.C.S.-M. and O.A.M.-L.; methodology, P.C.S.-M.; software, E.A.R.-M.; validation, E.A.R.-M. and P.C.S.-M.; formal analysis: M.G.-R. and L.E.A.-R.; data curation, E.A.R.-M.; writing--original draft preparation, P.C.S.-M. and E.A.R.-M.; writing--review and editing: M.G.-R., O.A.M.-L., and L.E.A.-R. All authors have read and agreed to the published version of the manuscript. This study received no external funding. Not applicable. Informed Consent Statement: Informed consent was obtained from all the subjects involved in the study. Data Availability Statement: The data presented in this study are available upon reasonable request from the corresponding author. The authors extend their gratitude to the volunteers who contributed their time and effort to participate in the gaming experience assessment and dataset creation. Their participation significantly affected the quality and depth of the research. The authors declare no conflict of interest. The player experience assessment instrument is presented next. General information  
Name  
Age  
Where do you regularly play Video Games? (You can mark more than one option)  
Home  
School Cybercafe  
Other  
What platform do you use to play Video Games on?