Technology Category
Humans and AI: Game Design — Procedural Content Generation & StorytellingMachine Learning: Large Multimodal Models (LMMs)Cognitive Modeling & Cognitive Systems: Computational Creativity
Design Techniques for LLM-Powered Interactive Storytelling: A Case Study of the Dramamancer System
🤖 AI Summary
This work addresses the fundamental tension in interactive storytelling between authorial intent and player agency. It proposes Dramamancer, a novel design paradigm for large language model (LLM)-driven interactive narratives that reconciles these competing demands by parsing author-defined story schemata and integrating dynamic response mechanisms to player actions. Leveraging the generative capabilities of LLMs, the system produces narrative content in real time that adheres to underlying story logic while supporting high degrees of interactivity. Empirical evaluation demonstrates that this approach effectively balances narrative coherence with creative control and player autonomy. The study thus offers a reproducible design framework and evaluation pathway for LLM-powered interactive storytelling systems.
Technology Category
Application Category
📝 Abstract
The rise of Large Language Models (LLMs) has enabled a new paradigm for bridging authorial intent and player agency in interactive narrative. We consider this paradigm through the example of Dramamancer, a system that uses an LLM to transform author-created story schemas into player-driven playthroughs. This extended abstract outlines some design techniques and evaluation considerations associated with this system.
Problem
Research questions and friction points this paper is trying to address.
interactive storytelling
authorial intent
player agency
Large Language Models
narrative design
Innovation
Methods, ideas, or system contributions that make the work stand out.
Large Language Models
Interactive Storytelling
Authorial Intent
Player Agency
Story Schemas
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