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ZENODO
Article . 2024
License: CC BY
Data sources: ZENODO
ZENODO
Article . 2024
License: CC BY
Data sources: Datacite
ZENODO
Article . 2024
License: CC BY
Data sources: Datacite
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Adaptive Worlds: Generative AI in Game Design and Future of Gaming, and Interactive Media

Authors: Jeremiah Ratican1*, James Hutson2;

Adaptive Worlds: Generative AI in Game Design and Future of Gaming, and Interactive Media

Abstract

Abstract Generative AI is revolutionizing the field of game design, introducing unprecedented adaptability and personalization in gameplay. The latest advancements in AI-driven engines enable real-time content creation, offering dynamic, player-driven experiences that diverge from traditional pre-programmed narratives. This shift marks a transition toward "choose your own adventure" formats, with an unlimited number of variations in levels, enemies, collectibles, and weaponry, tailored to each player's decisions. Google's GameNGen, for example, showcases AI's capacity to recreate classic games like DOOM, learning and generating gameplay in real time. These innovations are not restricted to gaming alone; they extend to edutainment, television, and film, where AI tools such as Cybever allow creators to generate 3D worlds from simple inputs like sketches. Such developments underscore a broader trend in AI’s role in shaping interactive media, providing new opportunities for personalized learning and entertainment experiences. The advent of tools like Notebook LM also blurs the lines between gaming and other media, enabling the creation of AI-written scripts and avatars, enhancing storytelling across platforms. This article explores the transformative potential of generative AI, emphasizing the implications for the future of edutainment, gaming, and beyond. Keywords: Generative AI, Game design, Real-time content generation, Interactive media, Personalized gaming experiences

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Average
Average
Average