REVIEW 2 cited by
VISAR: A Human-AI Argumentative Writing Assistant with Visual Programming and Rapid Draft Prototyping
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
read the original 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.
Forward citations
Cited by 2 Pith papers
-
Polymind: Parallel Visual Diagramming with Large Language Models to Support Prewriting Through Microtasks
Polymind introduces parallel, configurable LLM microtasks on a diagramming canvas for prewriting, and a small user study indicates it affords users more control and customization than turn-taking chatbot interaction.
-
MeetMap: Real-Time Collaborative Dialogue Mapping with LLMs in Online Meetings
A real-time collaborative dialogue mapping system with two levels of AI assistance improved meeting participants' sense-making and consensus compared to a transcript-plus-notes baseline.
Discussion (0). Continue with ORCID to comment.