AI as a Catalyst for Automation in High-Level Game Design for Adaptive Game Structures and Enhancing Player Engagement - Oleh Riazanov (Emerging Frontiers Library for The American Journal of Engineering and Technology, 2025)
The paper discusses the great change at the high end of game development brought about by generative artificial intelligence — not simply a minor tooling upgrade but rather a machine that can be used in automating the building of adaptive systems and increasing the gravitational pull of engagement for players. At a studio level, adoption has already moved beyond pilot projects to institutional practice-a shift that turns mere efficiency gains into matters of existence for quite many teams. The paper’s novel input is a neat merge of three paths: first, the lift of a game’s “skeleton” to a formal meta-model that lets big language models set up event graphs; second, a change in how hardship is made real — moving from simple time factors (D1/D7) to group-level keeping and leaving-risk hints so that tuning helps long-term involvement rather than short-term win rates; and third, the use of a feedback loop — watcher-helper sending possible changes to a skilled checker, with checker fixes sent back as top-notch training samples to lessen false guesses methodically. AI-fueled automation substantially speeds up prototyping and can enhance extended player engagement; however, these advances are precarious — dependent on legally verifiable data origin, strict dual-path (algorithmic + human) examination, and transparent intervention mechanisms — if not present, then adaptive systems may stray from customization to hidden control. This paper aims at academics and business professionals who seek disciplined, practical methods for implementing AI in design while maintaining creative freedom and responsibility.