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VISAR: A Human-AI Argumentative Writing Assistant with Visual Programming and Rapid Draft Prototyping

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Zhang, Zheng, Gao, Jie, Dhaliwal, Ranjodh Singh, Li, Toby Jia-Jun·2023·Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology
DOI 10.1145/3586183.3606800
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Abstract

In argumentative writing, writers must brainstorm hierarchical writing goals, ensure the persuasiveness of their arguments, and revise and organize their plans through drafting. Recent advances in large language models (LLMs) have made interactive text generation through a chat interface (e.g., ChatGPT) possible. However, this approach often neglects implicit writing context and user intent, lacks support for user control and autonomy, and provides limited assistance for sensemaking and revising writing plans. To address these challenges, we introduce VISAR, an AI-enabled writing assistant system designed to help writers brainstorm and revise hierarchical goals within their writing context, organize argument structures through synchronized text editing and visual programming, and enhance persuasiveness with argumentation spark recommendations. VISAR allows users to explore, experiment with, and validate their writing plans using automatic draft prototyping. A controlled lab study confirmed the usability and effectiveness of VISAR in facilitating the argumentative writing planning process.

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Cited by

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Related

Authors
Zhang, Z., Gao, J., Dhaliwal, R. S., & Li, T. J.-J.
Affiliations
University of Notre Dame (US)
ORCID
Venue
Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology
Publisher
ACM
Published
2023-10-29
Pages
1-30
DOI
10.1145/3586183.3606800
OpenAlex
W4385438855
Cited by
108
References
50
Citations by year
2026: 30 · 2025: 54 · 2024: 21 · 2023: 3
FWCI
25.06
Type
conference paper
Language
en
Open access
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor, 366 d)
Locations
  • ACM Symposium on User Interface Software and Technology (UIST) (publishedVersion)
  • arXiv (Cornell University) (submittedVersion, cc-by)
  • arXiv (Cornell University) (cc-by)
Funders
NSF (National Science Foundation) (CCF-2211428)
Topics
  • Physical Sciences › Computer Science › Human-Computer Interaction › Usability and User Interface Design
  • Physical Sciences › Computer Science › Human-Computer Interaction › Innovative Human-Technology Interaction
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Data Visualization and Analytics
Keywords
argumentative writing, writing assistants, large language models, visual programming, writing planning, text editing, user autonomy, sensemaking

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