# Game Plot Design with an LLM-powered Assistant: An Empirical Study with Game Designers

arXiv: https://arxiv.org/abs/2411.02714 · HTML: https://mlsanity.com/paper/2411.02714/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 30 references that are in the map.
- First posted on arXiv 5 Nov 2024.

## Builds on (most cited first, 8 of 8)

- [Co-Writing Screenplays and Theatre Scripts with Language Models: An Evaluation by Industry Professionals](https://mlsanity.com/paper/2209.14958/) (2022)
- [Interactive Fiction Games: A Colossal Adventure](https://mlsanity.com/paper/1909.05398/) (2019)
- [Creative Writing with an AI-Powered Writing Assistant: Perspectives from Professional Writers](https://mlsanity.com/paper/2211.05030/) (2022)
- [HelloBench: Evaluating Long Text Generation Capabilities of Large Language Models](https://mlsanity.com/paper/2409.16191/) (2024)
- [Large Language Models and Video Games: A Preliminary Scoping Review](https://mlsanity.com/paper/2403.02613/) (2024)
- [Player-Driven Emergence in LLM-Driven Game Narrative](https://mlsanity.com/paper/2404.17027/) (2024)
- [GENEVA: GENErating and Visualizing branching narratives using LLMs](https://mlsanity.com/paper/2311.09213/) (2023)
- [MCPDial: A Minecraft Persona-driven Dialogue Dataset](https://mlsanity.com/paper/2410.21627/) (2024)

## Built on by (most cited first, 4 of 4)

- [FAIRGAMER: Evaluating Social Biases in LLM-Based Video Game NPCs](https://mlsanity.com/paper/2508.17825/) (2025)
- [Systematizing LLM Persona Design: A Four-Quadrant Technical Taxonomy for AI Companion Applications](https://mlsanity.com/paper/2511.02979/) (2025)
- [Games That Teach, Chats That Convince: Comparing Interactive and Static Formats for Persuasive Learning](https://mlsanity.com/paper/2602.17905/) (2026)
- [RPA-Check: A Multi-Stage Automated Framework for Evaluating Dynamic LLM-based Role-Playing Agents](https://mlsanity.com/paper/2604.11655/) (2026)

## Abstract (arXiv)

We introduce GamePlot, an LLM-powered assistant that supports game designers in crafting immersive narratives for turn-based games, and allows them to test these games through a collaborative game play and refine the plot throughout the process. Our user study with 14 game designers shows high levels of both satisfaction with the generated game plots and sense of ownership over the narratives, but also reconfirms that LLM are limited in their ability to generate complex and truly innovative content. We also show that diverse user populations have different expectations from AI assistants, and encourage researchers to study how tailoring assistants to diverse user groups could potentially lead to increased job satisfaction and greater creativity and innovation over time.