SARD: A Human-AI Collaborative Story Generation
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Abstract (arXiv)
Generative artificial intelligence (GenAI) has ushered in a new era for storytellers, providing a powerful tool to ignite creativity and explore uncharted narrative territories. As technology continues to advance, the synergy between human creativity and AI-generated content holds the potential to redefine the landscape of storytelling. In this work, we propose SARD, a drag-and-drop visual interface for generating a multi-chapter story using large language models. Our evaluation of the usability of SARD and its creativity support shows that while node-based visualization of the narrative may help writers build a mental model, it exerts unnecessary mental overhead to the writer and becomes a source of distraction as the story becomes more elaborated. We also found that AI generates stories that are less lexically diverse, irrespective of the complexity of the story. We identified some patterns and limitations of our tool that can guide the development of future human-AI co-writing tools.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 10 of its 25 references that are in the map. First posted on arXiv 3 Mar 2024.
Builds on
10 of 25 references.
References · 10
- 2018Hierarchical Neural Story Generationcited by 1,182
- 2022Re3: Generating Longer Stories With Recursive Reprompting and Revisioncited by 193
- 2022Co-Writing Screenplays and Theatre Scripts with Language Models: An Evaluation by Industry Professionalscited by 191
- 2023Does Writing with Language Models Reduce Content Diversity?cited by 190
- 2023Art or Artifice? Large Language Models and the False Promise of Creativitycited by 168
- 2021Sparks: Inspiration for Science Writing using Language Modelscited by 80
- 2022Help me write a poem: Instruction Tuning as a Vehicle for Collaborative Poetry Writingcited by 71
- 2023On the Creativity of Large Language Modelscited by 57
- 2021Stylized Story Generation with Style-Guided Planningcited by 21
- 2023Learning to Generate Text in Arbitrary Writing Stylescited by 9
Built on by
Citing papers per year: 2024: 3; 2025: 3; 2026: 2.
8 papers.
- 2024A Character-Centric Creative Story Generation via Imaginationcited by 12
- 2025StorySage: Conversational Autobiography Writing Powered by a Multi-Agent Frameworkcited by 4
- 2025Constella: Supporting Storywriters' Interconnected Character Creation through LLM-based Multi-Agentscited by 4
- 2024LLM2TEA: An Agentic AI Designer for Discovery with Generative Evolutionary Multitaskingcited by 2
- 2024Arabic Automatic Story Generation with Large Language Modelscited by 1
- 2025AnimAgents: Coordinating Multi-Stage Animation Pre-Production with Human-Multi-Agent Collaborationcited by 1
- 2026From Intention to Text: AI-Supported Goal Setting in Academic Writingcited by 0
- 2026Semantic Prompting: Agentic Incremental Narrative Refinement through Spatial Semantic Interactioncited by 0
- 2026Semantic Prompting: Agentic Incremental Narrative Refinement through Spatial Semantic Interaction21 Apr 2026
- 2026From Intention to Text: AI-Supported Goal Setting in Academic Writing17 Apr 2026
- 2025AnimAgents: Coordinating Multi-Stage Animation Pre-Production with Human-Multi-Agent Collaboration22 Nov 2025
- 2025Constella: Supporting Storywriters' Interconnected Character Creation through LLM-based Multi-Agents8 Jul 2025
- 2025StorySage: Conversational Autobiography Writing Powered by a Multi-Agent Framework17 Jun 2025
- 2024A Character-Centric Creative Story Generation via Imagination25 Sep 2024
- 2024Arabic Automatic Story Generation with Large Language Models10 Jul 2024
- 2024LLM2TEA: An Agentic AI Designer for Discovery with Generative Evolutionary Multitasking21 Jun 2024