EXPLORING ONLINE COMMUNITY PERCEPTIONS OF MACHINE LEARNING USE IN ADJUSTING GAME DIFFICULTY LEVELS
DOI:
https://doi.org/10.58432/sfzn2g47Keywords:
Machine Learning, Dynamic Difficulty Adjustment, Sentiment Analysis, Topic ModelingAbstract
This study aims to explore the online community's perception of the use of machine learning (ML) in dynamic difficulty adjustment (DDA) through computational text analysis. The use of ML in DDA has become an important area in game design due to its ability to increase engagement and personalize the gaming experience, but it has sparked debate regarding the fairness and transparency of the system. This study uses a descriptive quantitative approach by analyzing 3,016 comments from the Reddit and Steam communities using sentiment analysis, topic modeling, and word frequency analysis techniques. The sentiment analysis results show a dominance of positive sentiment (63.8%), followed by negative sentiment (20.9%) and neutral sentiment (15.0%). Topic analysis identified five main themes: Gameplay & Difficulty Adjustment, General Enjoyment & Perception, Narrative Experience & Balance, Combat Mechanics & Fairness, and Technical Reflection & Suggestion. Word frequency analysis showed the community's focus on the balance between challenge (difficulty) and enjoyment (fun) in the gaming experience. The research findings indicate that the online community positively accepts the application of ML in DDA, with the important notes of system transparency, fairness of mechanisms, and balance between technical challenges and players' emotional satisfaction. This research contributes to interdisciplinary studies between game studies, artificial intelligence, and computational social science, and serves as a reference for game developers in designing adaptive systems that are technically effective and accepted by players.
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Fisher, N., & K. Kulshreshth, A. (2025). Dynamic Difficulty Adjustment in Games: Concepts, Techniques, and Applications. From Pixels to Play - The Art and Science of Video Games [Working Title]. https://doi.org/10.5772/intechopen.1011703
Gu, L., Shen, X., & Jia, A. L. (2021). User Activities in Game Replay Sharing Communities and Their Implications for User Retention. IEEE Transactions on Games, 13(2), 205–215. https://doi.org/10.1109/TG.2021.3058187
Hossan, M. M., Fouda, M. M., & Eishita, F. Z. (2024). Adaptive Game Design Using Machine Learning Techniques: A Survey. Proceedings of 2024 IEEE International Conference on Internet of Things and Intelligence Systems, IoTaIS 2024, 144–150. https://doi.org/10.1109/IoTaIS64014.2024.10799330
Januar Singgih Abdullah, & Agus Juhana. (2025). The Development and Effectiveness Evaluation of Game Labs in Learning: Content Analysis and the Impact of Gamification. DIAJAR: Jurnal Pendidikan Dan Pembelajaran, 4(1), 85–93. https://doi.org/10.54259/diajar.v4i1.3414
Mondal, D. (2025). Encouraging Digital Participation of Communities in Tourism Development: The Future of AI, VR, and Blockchain for Local Communities. SSRN. https://doi.org/https://dx.doi.org/10.2139/ssrn.5388167
Mortazavi, F., Moradi, H., & Vahabie, A. H. (2024). Dynamic difficulty adjustment approaches in video games: a systematic literature review. Multimedia Tools and Applications, 83(35), 83227–83274. https://doi.org/10.1007/s11042-024-18768-x
Naseer, F., Khan, M. N., Addas, A., Awais, Q., & Ayub, N. (2025). Game Mechanics and Artificial Intelligence Personalization: A Framework for Adaptive Learning Systems. Education Sciences, 15(3). https://doi.org/10.3390/educsci15030301
Paraschos, P. D., & Koulouriotis, D. E. (2025). Fuzzy Logic-Based Dynamic Difficulty Adjustment for Adaptive Game Environments. Electronics (Switzerland), 14(1). https://doi.org/10.3390/electronics14010146
Romero-Mendez, E. A., Santana-Mancilla, P. C., Garcia-Ruiz, M., Montesinos-López, O. A., & Anido-Rifón, L. E. (2023). The Use of Deep Learning to Improve Player Engagement in a Video Game through a Dynamic Difficulty Adjustment Based on Skills Classification. Applied Sciences (Switzerland), 13(14). https://doi.org/10.3390/app13148249
Sanaei, A. H. A. B. M., & Bozorgi-Amiri, A. (2025). Designing a personalized gamification social network platform using machine algorithms (clustering) (use case: user suggestion for doing data science learning projects in work teams). International Journal of Nonlinear Analysis and Applications. https://doi.org/10.22075/ijnaa.2023.31702.4701
Schelfhout, S., Bowers, M. T., & Hao, Y. A. (2021). Balancing Gender Identity and Gamer Identity: Gender Issues Faced by Wang ‘BaiZe’ Xinyu at the 2017 Hearthstone Summer Championship. Games and Culture, 16(1), 22–41. https://doi.org/10.1177/1555412019866348
Xiao, Q. (2025). The Influence of User Experience Satisfaction in VR Serious Games: Flow Experience and Self-efficacy as Mediating Effects. Springer Nature Switzerland.
Yujie, D., Zhou, T., & Xu, X. (2025). Research on Personalized Motion Difficulty Dynamic Adjustment Algorithm for VR Rehabilitation Sports Based on Reinforcement Learning. IEEE Access, 13(April), 107783–107799. https://doi.org/10.1109/ACCESS.2025.3579160
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Copyright (c) 2026 Mahesha Jenar Yushayahya, Muhammad Rizky Dirwansyah, Ilham Saputra, Arya Farija Putra, Muhammad Rezy Maulidiansyah (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
