A Comprehensive Review of the Use of Artificial Intelligence in Gaming

Abstract

Artificial Intelligence (AI) is fundamentally reshaping the landscape of gaming, serving as a superhuman competitor, a novel development tool, a sophisticated in-game agent, and a fertile training ground for advancing AI research itself. The strategic domain has been a key crucible for these advancements, with deep reinforcement learning (DRL) models like Google DeepMind\'s AlphaGo, AlphaGo Zero, and MuZero achieving superhuman performance in complex games such as Go, Chess, and Shogi. These models, often tested in environments like Atari games , evolved from requiring human expert data to learning purely from self-play, and ultimately to mastering games without any prior knowledge of their rules. The introduction of these superhuman AI-Powered Go Programs (APGs) provides a unique lens to study human-AI learning; analysis of over 749,000 professional moves reveals that human decision-making quality significantly improved post-APG release. Players demonstrated a genuine learning effect, showing higher alignment with AI\'s suggestions and markedly reducing the number and magnitude of errors, especially in the highly uncertain, early phases of the game. This instructional impact varies, with younger and less-skilled players showing the greatest gains. Beyond playing, AI is transforming game creation through Procedural Content Generation via Machine Learning (PCGML). To overcome challenges of controllability and data scarcity, a novel \"distillation\" method uses LLMs to synthetically label content from traditional PCG algorithms, creating large-scale datasets for training steerable, text-conditioned generative models for a \"Text-to-game-Map\" task. Furthermore, AI is being integrated as a platform within games, such as in tactical wargaming experiments with fully autonomous Robotic Combat Vehicles (RCVs). These wargames reveal critical tactical implications, highlighting the vulnerabilities of remotely operated systems to jamming and the human tendency to employ autonomous RCVs as expendable \"bait\" , thereby helping operators and engineers co-develop realistic requirements for future AI-enabled systems.

Introduction

Artificial Intelligence (AI) has transitioned from theory to a transformative force, reshaping gaming, professional work, and strategic decision-making. In gaming, AI serves multiple roles: superhuman competitor and instructor, creative content generator, and autonomous strategic agent. Early breakthroughs like AlphaGo and MuZero demonstrated self-learning and mastery of complex games, while AI now enhances human decision-making, improves gameplay quality, and serves as a teaching tool, particularly benefiting younger or less-skilled players.

AI also revolutionizes game creation through procedural content generation, using approaches like text-conditioned generative models and Large Language Models (LLMs) to automate content creation, shifting human roles from direct design to defining inputs and constraints. In strategic simulations and wargaming, AI agents allow complex scenario testing, revealing insights about human-AI interactions, tactics, and autonomous systems' performance.

Theoretical challenges arise in algorithmic and co-evolutionary game theory, where AI must navigate complex multi-agent interactions, resource allocation, and fairness, sometimes using co-evolutionary algorithms and generative AI to automate strategy formulation and solutions.

Beyond gaming, AI is applied in software development, healthcare, networking, and HCI, enabling intelligent testing, personalized learning, adaptive rehabilitation, and creative content generation. This integration raises societal and ethical questions regarding persuasive power, workplace surveillance, and regulation, requiring careful navigation between “bright imagining” (disconnected from ethical concerns) and “dark imagining” (embedding potential harms as design constraints).

Collectively, AI is reshaping creativity, strategy, and human-AI interaction, while generating both practical opportunities and complex moral, regulatory, and technical challenges.

Conclusion

The role of Artificial Intelligence in strategic and creative domains is evolving rapidly, presenting a complex interplay of technical mastery, human-AI collaboration, and significant moral considerations. Games, in particular, serve as a critical training ground, where Reinforcement Learning models have demonstrated a clear progression from mastering complex strategy games with human data (AlphaGo) to learning \"tabula rasa\" (AlphaGo Zero) and even mastering environments without prior knowledge of their rules (Shaheen et al., 2025). The analysis of these complex strategic interactions requires a deep understanding of their underlying structure, such as computing game symmetries and the Nash equilibria that respect them, a task with its own significant computational complexity (Tewolde et al., 2025). When these AI agents are placed in simulations with human operators, such as tactical wargames, novel strategies emerge; for instance, human participants consistently employ fully autonomous platforms as \"expendable bait\" in ways they would not use remotely-operated or human-crewed assets (Tarraf et al., 2020). Beyond strategic applications, AI is also being framed as a \"prosthetic apparatus\" for human creativity, shifting the designer\'s role from \"product design\" to \"process design,\" where the human curates the inputs and constraints for the AI (Rigillo, 2023). This creative act is not morally neutral; AI creators must navigate the potential for harm by either disconnecting from the moral consequences (\"bright imagining\") or integrating them as creative constraints (\"dark imagining\") (Harvey, 2025). This moral and societal dimension is critical, as the field of AI research is at high risk of repeating the same methodological failures—such as using poor constructs and failing to establish causal inference—that have hindered the study of social media\'s impact on youth (Mansfield et al., 2025).

References

[1] M. A. Shaheen, M. B. Khan, and S. A. R. R. Naqvi, \"Reinforcement Learning in Strategy-Based and Atari Games: A Review of Google DeepMind\'s Innovations,\" Artif. Intell. Rev., 2025, doi: 10.1007/s43093-025-00423-y. [2] J. Mohapatra, \"Intelligent Assurance - A New Age Software Testing,\" Int. J. Emerg. Technol. Adv. Eng., vol. 12, no. 1, pp. 23–28, 2022. [3] G. N. Yannakakis and J. Togelius, \"Player modeling,\" in Artificial and Computational Intelligence in Games, 2013, pp. 1–20. [4] F. Stasolla et al., \"Exploring the potential of deep learning-based serious games combined with reinforcement learning for neurodevelopmental disorders,\" Front. Hum. Neurosci., vol. 19, Art. no. 1557826, 2025, doi: 10.3389/fnhum.2025.1557826. [5] A. F. Gomes et al., \"The effect of anthropomorphism in artificial intelligence on purchase intention: The role of customer engagement,\" SSRN Electron. J., 2025, doi: 10.2139/ssrn.5372806. [6] Y. Zhang, Q. Zhao, J. Wang, and S. Li, \"AI in the game: a hybrid machine learning model for sales forecasting in the gaming industry,\" Front. Public Health, vol. 13, Art. no. 1510801, 2025, doi: 10.3389/fpubh.2025.1510801. [7] C. He et al., \"Generative AI for Game Theory-based Mobile Networking: A Diffusion-based Game Approach,\" Preprints, 2025, Art. no. 2025081627, doi: 10.20944/preprints202508.1627.v1. [8] M. P. Wellman, \"Empirical Game-Theoretic Analysis,\" in Handbook of Computational Economics, vol. 4, P. E. T. and L. C. W., Eds. Elsevier, 2025, in press. [9] V. Soni and N. Nigam, \"AI for Software Testing: A Systematic Literature Review and Future Research Directions,\" arXiv, 2404.09699v2, 2025. [10] The Storytelling AI, AI Storytelling: A Creative Partner. transcript Verlag, 2025, doi: 10.14361/9783839474624. [11] A. Zaidi, S. Rais, A. A. Zaidi, A. Z. Arfeen, and M. S. Farooqi, \"Current practices and future direction of artificial intelligence in mathematics education: a systematic review,\" Asian J. Educ. Soc. Stud., vol. 16, no. 1, pp. 10–25, 2025, doi: 10.5281/zenodo.11666993. [12] A. Al-Marrif, A. M. H. Al-Marrif, H. H. Al-Marrif, S. R. H. Al-Marrif, and M. A. Al-Marrif, \"Artificial Intelligence and Physics-Based Anomaly Detection in the Smart Grid: A Survey,\" IEEE Access, 2025, doi: 10.1109/ACCESS.2025.10545295. [13] Z. E. Al-Abbasi, S. S. Al-Abbasi, H. H. Al-Abbasi, and M. A. Al-Abbasi, \"Towards Knowledgeable Agents: Large Language Models for Networking,\" in Proc. Int. Conf. Comput. Appl. Smart Agent. (ICCASA), 2024, pp. 75–87. [14] S. Setia, R. Bhat, R. U. Khan, A. S. B. H. M. Diah, and S. Singh, \"Generative Artificial Intelligence for Human-Computer Interaction: Enhancing User Experience, Personalization, and Accessibility,\" IEEE Access, 2024, doi: 10.1109/ACCESS.2024.10545295. [15] E. Giansante, \"Artificial Intelligence and its Persuasive Power: Redefining Communication in the Digital Age,\" arXiv, 2209.03499v3, 2025. [16] A. Harvey, \"Algorithmic Disciplining: A New Form of Managerial Control,\" Acad. Manage. J., 2025. [17] M. A. Shaheen, M. B. Khan, and S. A. R. R. Naqvi, \"Bright and Dark Imagining: How Creators Navigate Moral Consequences of Developing Ideas in Artificial Intelligence,\" arXiv, 2502.10303v1, 2025. [18] D. C. Tarraf, B. M. Williams, J. E. Ling, R. A. Rodriguez, and P. J. K. II, \"An Experiment in Tactical Wargaming with Platforms Enabled by Artificial Intelligence,\" RAND Corporation, 2020. [19] R. Mansfield et al., \"From Social Media to Artificial Intelligence: Improving Research on Digital Harms in Youth,\" Lancet Digit. Health, 2025. [20] S. Felz, H. H. Felz, and M. A. Felz, \"Regulating eXplainable Artificial Intelligence (XAI) May Harm Consumers,\" Mark. Sci., 2025. Here are the references for the 5 files you provided, formatted in the IEEE style: [21] M. Rigillo, \"Architecture in the Age of Artificial Intelligence. An Introduction to AI for Architects,\" AIS Associazione Italiana di Sociologia, 2023. [22] S. Felz, H. H. Felz, and M. A. Felz, \"Regulating eXplainable Artificial Intelligence (XAI) May Harm Consumers,\" Mark. Sci., 2025. [23] C. He et al., \"Generative AI for Game Theory-based Mobile Networking: A Diffusion-based Game Approach,\" arXiv, 2310.08704v2, 2024. [24] M. A. Shaheen, M. B. Khan, and S. A. R. R. Naqvi, \"Bright and Dark Imagining: How Creators Navigate Moral Consequences of Developing Ideas in Artificial Intelligence,\" IEEE Trans. Technol. Soc., 2025. [25] G. Tewolde, H. H. Tewolde, S. R. H. Tewolde, and M. A. Tewolde, \"Computing Game Symmetries and Equilibria That Respect Them,\" arXiv, 2409.17506v1, 2025.

Copyright

Copyright © 2025 Dr. Goldi Soni, Krishna Agrawal, Harshal Chaudhary . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.