Last month, the Anbernic RG557 was released, and with it came a surprising feature: “Anbernic AI,” a chatbot integrated right into the handheld console. While on the surface it seemed like a simple assistant, it turned out to be powered by large language models like Deepseek or Alibaba’s Qwen. This raises questions about data use and trust, but also sparks curiosity. Could players build something better themselves?
That led to attempts to build a better version using tools like NotebookLM, which was used to create a Dungeons & Dragons-style campaign complete with real-time suggestions, narrative pacing, and in-game lore support. That’s just one example, though. Underneath it all, the same mechanics driving those game guides are pushing change across other sectors of gaming too, from procedural world generation to complex NPC behaviour modelling.
The global artificial intelligence in game development market is projected to grow from USD 2.6 billion in 2025 to USD 25.3 billion by 2034 (around AUD 38.2 billion), showing just how much investment and interest this tech is drawing.
Personalisation in Gaming
While many developers are focused on creating more immersive console and PC titles, other sectors have been quicker to adopt this tech in customer-facing ways. Gambling websites, gaming libraries, and free-to-play online websites are all using machine learning to improve user experience, especially in places where competition is fierce.
According to Jan Vermeer, any online casino in the Netherlands worth visiting uses AI for personalisation. These systems can analyse a player’s habits and then suggest games, betting options, and rewards that match individual preferences. That approach often leads to longer engagement and better retention for the platforms, while offering players a more relevant selection of options to choose from. Once those same behavioural models are applied to mainstream games, it opens up new potential for customisation far beyond difficulty settings.
Machine Learning Behind the Scenes
While the front-end visuals of most games still grab attention, the real changes are happening under the hood. Developers are beginning to use natural language models to write dialogue and to fine-tune how game worlds react to players. Today’s interactions are driven by data, player decisions, and context-aware systems.
Studios are also using predictive tools to model how players might act in certain situations, especially in role-playing and strategy games. This lets developers fine-tune difficulty, pacing, and even emotional tone in real time. The result is a gaming experience that feels more reactive, more tailored, and less like you’re just walking through someone else’s story.
Smarter Non-Player Characters
One of the most promising applications is in NPC behaviour. Traditional non-player characters follow set patterns. Maybe they pace around, offer a canned dialogue line, and disappear. Using learning-based systems, characters can now respond to player tone, anticipate choices, and adjust their behaviour dynamically. In some early indie projects, AI-driven NPCs even learn over time, responding differently after repeated interactions.
Game Worlds That Shape Themselves
Procedural generation isn’t new, but combining it with machine learning brings a different feel to level and environment design. Instead of spitting out random terrain, these tools can now generate maps, buildings, and scenarios that reflect how the player plays. For example, a player who prefers stealth may find future missions offering more alternative paths and fewer head-on combat setups.
What used to be the domain of modders and fan-made content is slowly making its way into official titles. In the long term, this could mean game worlds that grow with players across entire franchises, reacting not just to decisions in a single title but carrying over behavioural data across multiple entries.
Voice Interaction and Real-Time Feedback
Another space where this tech is showing up more is in voice interaction. Games are starting to integrate live voice control and feedback systems, not just for accessibility but to make dialogue trees more fluid. Rather than picking pre-written options from a menu, some prototypes now let players speak naturally, with the system responding in kind. It’s not flawless yet, but it’s promising.
In a multiplayer setting, there’s even potential for real-time translation and coaching, systems that can help players understand unfamiliar mechanics or foreign-language teammates on the fly. These might be subtle changes, but they can affect the feel of a session quite a bit. Mobile devices are smarter and faster than ever, and with advances in tech, all of this can now happen straight from your phone.
What’s Next?
As new hardware like the PlayStation 5 and upcoming PC GPUs continues to push graphical fidelity, the real change may not be visual. The addition of learning-based systems into mainstream engines could mark a turning point where gameplay changes based on who’s playing and not just what’s been coded.
Gemini’s D&D use case might feel like a novelty, but it’s also a proof of concept. As generative tools continue to advance, we’re going to see a lot more titles shaped not only by developers but by player data itself. That makes gaming less about mastering static mechanics and more about being part of an experience that learns with you.
