Interactive Narrative Systems (INSs) have emerged as a powerful medium for creating engaging and immersive digital experiences, allowing users to influence a dramatic storyline through actions and decisions. However, giving users more freedom within these systems comes at the cost of authorial control, leading to the well-known Narrative Paradox, which makes these systems less suitable for specific contexts such as training and education. This thesis introduces a novel approach that leverages Large Language Models (LLMs) to address this paradox and enhance users’ experience with INSs. We developed a Dynamic Interactive Narrative System Integrating LLMs (DINSIL), which exploits this technology to generate contextually relevant and creative responses to users’ actions, expanding their possibilities for interaction while the narrative unfolds, making each experience specifically tailored to the player.