Generative Artificial Intelligence in Digital Game Design and Development: A Bibliometric and Complex Network Analysis
Resumo
Introduction: Generative AI (GenAI) has reshaped digital game design since 2017, yet the structural roles of its core concepts remain unmapped. Objective: This study maps GenAI in digital games (2018–2025) using bibliometric and keyword co-occurrence network analysis. Methodology: From 111 peer-reviewed articles (PRISMA-adapted pipeline, LLM-based semantic screening + human validation, Cohen’s ? = 0.60), we applied four centrality measures and Louvain community detection. Results: The network (29 nodes, 103 edges) shows atypical topology — density 0.254, clustering 0.675, modularity 0.136, 62.1% inter-community weight — with all centralities converging on five hubs: game, generative model, PCG, training, and LLM. Two anchors emerge: game (domain anchor, highest degree/eigenvector) and generative model (technology anchor, betweenness 2.8× higher). Both findings are robust across nine threshold configurations and 100 Louvain runs. Lexical analysis shows a paradigm shift: LLMs (+3.47 pp) and diffusion models (+1.28 pp) ascend, while GANs and generic deep-learning vocabulary dilute. The field is highly integrated, anchored by an umbrella technological concept, in early modular specialization. Search strings, screening prompt, per-record decisions, and reproduction scripts are publicly available.
Palavras-chave:
Generative Artificial Intelligence, Digital Games, Bibliometric Analysis, Complex Networks, Procedural Content Generation
Referências
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Bontrager, P. e Togelius, J. (2021). Learning to generate levels from nothing. In 2021 IEEE Conference on Games (CoG), pages 1–8.
Buakhaw, P., Kerdthaisong, K., Phenhiran, P., Khlaisamniang, P., Vorathammathorn, S., Ittichaiwong, P., e Yongsatianchot, N. (2025). Deflanderization for game dialogue: Balancing character authenticity with task execution in LLM-based NPCs. arXiv preprint arXiv:2510.13586.
Buongiorno, S., Klinkert, L. J., Chawla, T., Zhuang, Z., e Clark, C. (2024). PANGeA: Procedural artificial narrative using generative AI for turn-based video games. arXiv preprint arXiv:2404.19721.
Callison-Burch, C., Tomar, G. S., Ippolito, D., Martin, L. J., Bailis, S., e Reitter, D. (2022). Dungeons and Dragons as a dialog challenge for artificial intelligence. arXiv preprint arXiv:2210.07109.
Chen, D. Z. et al. (2023). Text2Tex: Text-driven texture synthesis via diffusion models. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV).
Čišić, D., Drezgić, S., e Čegar, S. (2025). A bibliometric analysis of artificial intelligence in human–computer interaction. In 2025 48th MIPRO ICT and Electronics Convention (MIPRO). IEEE.
Clauset, A., Newman, M. E. J., e Moore, C. (2004). Finding community structure in very large networks. Physical Review E, 70(6):066111.
Cobo, M. J., López-Herrera, A. G., Herrera-Viedma, E., e Herrera, F. (2011). Science mapping software tools: Review, analysis, and cooperative study among tools. Journal of the American Society for Information Science and Technology, 62(7):1382–1402.
Ding, Y., Chowdhury, G. G., e Foo, S. (2001). Bibliometric cartography of information retrieval research by using co-word analysis. Information Processing and Management, 37(6):817–842.
Fonseca, C. V. C., Predo, R. M., e Blikstad, N. M. D. (2025). Defining a research agenda for Industry 4.0 technologies in corporate strategies: insights from complex networks and machine learning. International Journal of Production Research, 63(23):8743–8760.
Gabani, K., Pathak, H., Thakkar, I., Jain, N., Rajendran, S., e Mukherjee, D. (2023). Dynamic terrain generation with deep GANs. pages 61–69.
Ho, J., Jain, A., e Abbeel, P. (2020). Denoising diffusion probabilistic models. In Advances in Neural Information Processing Systems (NeurIPS), pages 6840–6851.
Hu, X., Xing, Y., Cai, X., Zhao, Y., Cook, M., Borgo, R., e Neate, T. (2025). Designing interactions with generative AI for art and creativity: A systematic review and taxonomy. In Proceedings of the 2025 ACM Designing Interactive Systems Conference (DIS). ACM.
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Kumaran, V., Rowe, J., Mott, B., e Lester, J. (2023). SceneCraft: Automating interactive narrative scene generation in digital games with large language models. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, 19(1):86–96.
Lao, A. (2025). A systematic review of AI-driven game design and user experience research. Humanities and Social Science Research, 8(4):229.
Li, Z. (2025). Design and application of a dynamic NPC system based on local LLMs. Transactions on Computer Science and Information Systems Research.
Liu, J., Snodgrass, S., Khalifa, A., Yannakakis, G. N., e Togelius, J. (2021). Deep learning for procedural content generation. Neural Computing and Applications, 33:19–37.
Liu, X. et al. (2025). Cross-modal AIGC integration in Unity3D for game art creation. Electronics, 14(6).
Meira-Rodríguez, J. (2025). Creación de ambientes para niveles de videojuegos utilizando Stable Diffusion. European Public and Social Innovation Review.
Muñoz-Leiva, F., Viedma-del Jesús, M. I., Sánchez-Fernández, J., e López-Herrera, A. G. (2012). An application of co-word analysis and bibliometric maps for detecting the most highlighting themes in the consumer behaviour research from a longitudinal perspective. Quality and Quantity, 46(4):1077–1095.
Ni, X., Liu, J., Cao, X., e Wang, C. (2019). Tracking the dynamics of co-word networks for emerging topic identification. Information Processing and Management, 56(4):102043.
Nie, Y., Middleton, M., Merino, T., Kanagaraja, N., Kumar, A., Zhuang, Z., e Togelius, J. (2025). Moonshine: Distilling game content generators into steerable generative models. Proceedings of the AAAI Conference on Artificial Intelligence, 39(13):14344–14351.
NVIDIA (2024). Edify 3D: Scalable high-quality 3D asset generation. arXiv preprint arXiv:2411.07135.
Özkan, M. B. (2025). Procedural game level design with deep reinforcement learning. arXiv preprint arXiv:2510.15120.
Radhakrishnan, S., Erbis, S., Isaacs, J. A., e Kamarthi, S. (2017). Novel keyword co-occurrence network-based methods to foster systematic reviews of scientific literature. PLoS ONE, 12(3):e0172778.
Schrum, J. et al. (2020). Interactive evolution and exploration within latent level-design space of generative adversarial networks. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO). ACM.
Singh, A. et al. (2024a). Systematic review of GAN for enhancing efficiency in AI in gaming. In Proceedings of the IEEE ACROSET.
Singh, R. et al. (2024b). Enhancing game development: A comparative analysis of image generative AI. In Proceedings of the IEEE MetroXRAINE.
Sudhakaran, S., González-Duque, M., Glanois, C., Najarro, E., Freiberger, S., e Risi, S. (2023). MarioGPT: Open-ended text2level generation through large language models. arXiv preprint arXiv:2302.05981.
Sweetser, P. (2024). Large language models and video games: A preliminary scoping review. arXiv preprint arXiv:2403.02613.
Ternar, D.-A., Denisova, A., Cunha, J. M., Kultima, A., e Guckelsberger, C. (2026). Generative AI in game development: A qualitative research synthesis. In Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems. ACM.
Tricco, A. C., Lillie, E., Zarin, W., O’Brien, K. K., Colquhoun, H., Levac, D., et al. (2018). PRISMA extension for scoping reviews (PRISMA-ScR): Checklist and explanation. Annals of Internal Medicine, 169(7):467–473.
Trukhin, R., Frenkel Isaksson, J., e Eladhari, M. P. (2025). LLM-powered NPCs: Evaluating the impact of large language models on NPC design. In Interactive Storytelling (ICIDS), volume 16375 of Lecture Notes in Computer Science, pages 171–189. Springer.
van Eck, N. J. e Waltman, L. (2014). Visualizing bibliometric networks. In Ding, Y., Rousseau, R., e Wolfram, D., editors, Measuring Scholarly Impact: Methods and Practice, pages 285–320. Springer, Cham.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., e Polosukhin, I. (2017). Attention is all you need. In Advances in Neural Information Processing Systems (NeurIPS), pages 5998–6008.
Wu, X. (2025). Leveraging Stable Diffusion for enhanced game asset generation pipelines. International Journal of Advance in Applied Science Research, 4(12).
Wu, Z., Chen, Z., Zhu, D., Mousas, C., e Kao, D. (2025). A systematic review of generative AI on game character creation: Applications, challenges, and future trends. IEEE Transactions on Games.
Xia, Y. (2025). Generative AI in game development: A bibliometric study with an auditable computing workflow. In Proceedings of the 2025 International Conference on Artificial Intelligence, Virtual Reality and Interaction Design. ACM.
Yang, D., Kleinman, E., e Harteveld, C. (2024). GPT for games: A scoping review (2020–2023). In 2024 IEEE Conference on Games (CoG), pages 1–8.
Yang, D., Kleinman, E., e Harteveld, C. (2025). GPT for games: An updated scoping review (2020–2024). IEEE Transactions on Games.
Zhang, J., Xie, J., Hou, W., Tu, X., Xu, J., Song, F., Wang, Z., e Lu, Z. (2012). Mapping the knowledge structure of research on patient adherence: knowledge domain visualization based co-word analysis and social network analysis. PLoS ONE, 7(4):e34497.
Blondel, V. D., Guillaume, J.-L., Lambiotte, R., e Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008(10):P10008.
Bontrager, P. e Togelius, J. (2021). Learning to generate levels from nothing. In 2021 IEEE Conference on Games (CoG), pages 1–8.
Buakhaw, P., Kerdthaisong, K., Phenhiran, P., Khlaisamniang, P., Vorathammathorn, S., Ittichaiwong, P., e Yongsatianchot, N. (2025). Deflanderization for game dialogue: Balancing character authenticity with task execution in LLM-based NPCs. arXiv preprint arXiv:2510.13586.
Buongiorno, S., Klinkert, L. J., Chawla, T., Zhuang, Z., e Clark, C. (2024). PANGeA: Procedural artificial narrative using generative AI for turn-based video games. arXiv preprint arXiv:2404.19721.
Callison-Burch, C., Tomar, G. S., Ippolito, D., Martin, L. J., Bailis, S., e Reitter, D. (2022). Dungeons and Dragons as a dialog challenge for artificial intelligence. arXiv preprint arXiv:2210.07109.
Chen, D. Z. et al. (2023). Text2Tex: Text-driven texture synthesis via diffusion models. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV).
Čišić, D., Drezgić, S., e Čegar, S. (2025). A bibliometric analysis of artificial intelligence in human–computer interaction. In 2025 48th MIPRO ICT and Electronics Convention (MIPRO). IEEE.
Clauset, A., Newman, M. E. J., e Moore, C. (2004). Finding community structure in very large networks. Physical Review E, 70(6):066111.
Cobo, M. J., López-Herrera, A. G., Herrera-Viedma, E., e Herrera, F. (2011). Science mapping software tools: Review, analysis, and cooperative study among tools. Journal of the American Society for Information Science and Technology, 62(7):1382–1402.
Ding, Y., Chowdhury, G. G., e Foo, S. (2001). Bibliometric cartography of information retrieval research by using co-word analysis. Information Processing and Management, 37(6):817–842.
Fonseca, C. V. C., Predo, R. M., e Blikstad, N. M. D. (2025). Defining a research agenda for Industry 4.0 technologies in corporate strategies: insights from complex networks and machine learning. International Journal of Production Research, 63(23):8743–8760.
Gabani, K., Pathak, H., Thakkar, I., Jain, N., Rajendran, S., e Mukherjee, D. (2023). Dynamic terrain generation with deep GANs. pages 61–69.
Ho, J., Jain, A., e Abbeel, P. (2020). Denoising diffusion probabilistic models. In Advances in Neural Information Processing Systems (NeurIPS), pages 6840–6851.
Hu, X., Xing, Y., Cai, X., Zhao, Y., Cook, M., Borgo, R., e Neate, T. (2025). Designing interactions with generative AI for art and creativity: A systematic review and taxonomy. In Proceedings of the 2025 ACM Designing Interactive Systems Conference (DIS). ACM.
Korsah, A. (2025). The impact of generative artificial intelligence in game development: A scoping review. Master’s thesis, University of Oulu, Oulu, Finland.
Kumaran, V., Rowe, J., Mott, B., e Lester, J. (2023). SceneCraft: Automating interactive narrative scene generation in digital games with large language models. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, 19(1):86–96.
Lao, A. (2025). A systematic review of AI-driven game design and user experience research. Humanities and Social Science Research, 8(4):229.
Li, Z. (2025). Design and application of a dynamic NPC system based on local LLMs. Transactions on Computer Science and Information Systems Research.
Liu, J., Snodgrass, S., Khalifa, A., Yannakakis, G. N., e Togelius, J. (2021). Deep learning for procedural content generation. Neural Computing and Applications, 33:19–37.
Liu, X. et al. (2025). Cross-modal AIGC integration in Unity3D for game art creation. Electronics, 14(6).
Meira-Rodríguez, J. (2025). Creación de ambientes para niveles de videojuegos utilizando Stable Diffusion. European Public and Social Innovation Review.
Muñoz-Leiva, F., Viedma-del Jesús, M. I., Sánchez-Fernández, J., e López-Herrera, A. G. (2012). An application of co-word analysis and bibliometric maps for detecting the most highlighting themes in the consumer behaviour research from a longitudinal perspective. Quality and Quantity, 46(4):1077–1095.
Ni, X., Liu, J., Cao, X., e Wang, C. (2019). Tracking the dynamics of co-word networks for emerging topic identification. Information Processing and Management, 56(4):102043.
Nie, Y., Middleton, M., Merino, T., Kanagaraja, N., Kumar, A., Zhuang, Z., e Togelius, J. (2025). Moonshine: Distilling game content generators into steerable generative models. Proceedings of the AAAI Conference on Artificial Intelligence, 39(13):14344–14351.
NVIDIA (2024). Edify 3D: Scalable high-quality 3D asset generation. arXiv preprint arXiv:2411.07135.
Özkan, M. B. (2025). Procedural game level design with deep reinforcement learning. arXiv preprint arXiv:2510.15120.
Radhakrishnan, S., Erbis, S., Isaacs, J. A., e Kamarthi, S. (2017). Novel keyword co-occurrence network-based methods to foster systematic reviews of scientific literature. PLoS ONE, 12(3):e0172778.
Schrum, J. et al. (2020). Interactive evolution and exploration within latent level-design space of generative adversarial networks. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO). ACM.
Singh, A. et al. (2024a). Systematic review of GAN for enhancing efficiency in AI in gaming. In Proceedings of the IEEE ACROSET.
Singh, R. et al. (2024b). Enhancing game development: A comparative analysis of image generative AI. In Proceedings of the IEEE MetroXRAINE.
Sudhakaran, S., González-Duque, M., Glanois, C., Najarro, E., Freiberger, S., e Risi, S. (2023). MarioGPT: Open-ended text2level generation through large language models. arXiv preprint arXiv:2302.05981.
Sweetser, P. (2024). Large language models and video games: A preliminary scoping review. arXiv preprint arXiv:2403.02613.
Ternar, D.-A., Denisova, A., Cunha, J. M., Kultima, A., e Guckelsberger, C. (2026). Generative AI in game development: A qualitative research synthesis. In Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems. ACM.
Tricco, A. C., Lillie, E., Zarin, W., O’Brien, K. K., Colquhoun, H., Levac, D., et al. (2018). PRISMA extension for scoping reviews (PRISMA-ScR): Checklist and explanation. Annals of Internal Medicine, 169(7):467–473.
Trukhin, R., Frenkel Isaksson, J., e Eladhari, M. P. (2025). LLM-powered NPCs: Evaluating the impact of large language models on NPC design. In Interactive Storytelling (ICIDS), volume 16375 of Lecture Notes in Computer Science, pages 171–189. Springer.
van Eck, N. J. e Waltman, L. (2014). Visualizing bibliometric networks. In Ding, Y., Rousseau, R., e Wolfram, D., editors, Measuring Scholarly Impact: Methods and Practice, pages 285–320. Springer, Cham.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., e Polosukhin, I. (2017). Attention is all you need. In Advances in Neural Information Processing Systems (NeurIPS), pages 5998–6008.
Wu, X. (2025). Leveraging Stable Diffusion for enhanced game asset generation pipelines. International Journal of Advance in Applied Science Research, 4(12).
Wu, Z., Chen, Z., Zhu, D., Mousas, C., e Kao, D. (2025). A systematic review of generative AI on game character creation: Applications, challenges, and future trends. IEEE Transactions on Games.
Xia, Y. (2025). Generative AI in game development: A bibliometric study with an auditable computing workflow. In Proceedings of the 2025 International Conference on Artificial Intelligence, Virtual Reality and Interaction Design. ACM.
Yang, D., Kleinman, E., e Harteveld, C. (2024). GPT for games: A scoping review (2020–2023). In 2024 IEEE Conference on Games (CoG), pages 1–8.
Yang, D., Kleinman, E., e Harteveld, C. (2025). GPT for games: An updated scoping review (2020–2024). IEEE Transactions on Games.
Zhang, J., Xie, J., Hou, W., Tu, X., Xu, J., Song, F., Wang, Z., e Lu, Z. (2012). Mapping the knowledge structure of research on patient adherence: knowledge domain visualization based co-word analysis and social network analysis. PLoS ONE, 7(4):e34497.
Publicado
29/09/2026
Como Citar
SILVESTRINI, Murilo Mazzotti; SALMAZE, Pedro Henrique; PEREIRA, Leonardo Tórtoro.
Generative Artificial Intelligence in Digital Game Design and Development: A Bibliometric and Complex Network Analysis. In: SIMPÓSIO BRASILEIRO DE JOGOS E ENTRETENIMENTO DIGITAL (SBGAMES), 25. , 2026, Goiânia/GO.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 941-953.
DOI: https://doi.org/10.5753/sbgames.2026.26160.
