Design Techniques for LLM-Powered Interactive Storytelling: A Case Study of the Dramamancer System

arXiv (Cornell University) 2026 Preprint Open access

Read full text
Authors: Tiffany Wang, Yuqian Sun, Yi Wang, Melissa Roemmele, John Joon Young Chung, Max Kreminski
DOI: 10.48550/arxiv.2601.18785
Cited by: 0

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.

Fields of Study & Subjects

Artificial Intelligence in Games Narrative Theory and Analysis Digital Games and Media

Publication details

Journal / source
arXiv (Cornell University)
Publication date
26 January 2026
Document type
Preprint