# Effective Interfaces for Student-Driven Revision Sessions for Argumentative Writing

arXiv: https://arxiv.org/abs/2107.07018 · HTML: https://mlsanity.com/paper/2107.07018/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jul 2021.

## Built on by (most cited first, 12 of 12)

- [A Design Space for Intelligent and Interactive Writing Assistants](https://mlsanity.com/paper/2403.14117/) (2024)
- [Co-Writing with AI, on Human Terms: Aligning Research with User Demands Across the Writing Process](https://mlsanity.com/paper/2504.12488/) (2025)
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- [ArgRewrite V.2: an Annotated Argumentative Revisions Corpus](https://mlsanity.com/paper/2206.01677/) (2022)
- [Persua: A Visual Interactive System to Enhance the Persuasiveness of Arguments in Online Discussion](https://mlsanity.com/paper/2204.07741/) (2022)
- [StorySage: Conversational Autobiography Writing Powered by a Multi-Agent Framework](https://mlsanity.com/paper/2506.14159/) (2025)
- [Narrix: Remixing Narrative Strategies from Examples for Story Writing](https://mlsanity.com/paper/2604.07643/) (2026)
- [TYPIC: A Corpus of Template-Based Diagnostic Comments on Argumentation](https://mlsanity.com/paper/2201.06674/) (2022)
- [CoLyricist: Enhancing Lyric Writing with AI through Workflow-Aligned Support](https://mlsanity.com/paper/2602.22606/) (2026)
- [From Use to Oversight: How Mental Models Influence User Behavior and Output in AI Writing Assistants](https://mlsanity.com/paper/2604.05166/) (2026)
- [Teach Me How to Improve My Argumentation Skills: A Survey on Feedback in Argumentation](https://mlsanity.com/paper/2307.15341/) (2023)
- [Towards Cognitive Process-Aware Proactive Writing Support](https://mlsanity.com/paper/2608.30424/) (2026)

## Abstract (arXiv)

We present the design and evaluation of a web-based intelligent writing assistant that helps students recognize their revisions of argumentative essays. To understand how our revision assistant can best support students, we have implemented four versions of our system with differences in the unit span (sentence versus sub-sentence) of revision analysis and the level of feedback provided (none, binary, or detailed revision purpose categorization). We first discuss the design decisions behind relevant components of the system, then analyze the efficacy of the different versions through a Wizard of Oz study with university students. Our results show that while a simple interface with no revision feedback is easier to use, an interface that provides a detailed categorization of sentence-level revisions is the most helpful based on user survey data, as well as the most effective based on improvement in writing outcomes.