Automated Reformulation of Argumentative Essays to Improve Argument Organization and Development

2026-04-12 · Source: Paper Index on ACL Anthology · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Natural Language Processing, Data Science & Analytics · Depth: Advanced, quick

Summary

A study explores the automated reformulation of argumentative essays written by college-bound native Brazilian Portuguese speakers, aiming to provide pedagogical feedback. Researchers evaluated the feasibility of using large language models (LLMs) to score argument quality based on three criteria: defense of a point of view, organization, and development. An LLM was then employed to generate reformulated essay versions as feedback. A key challenge involved constraining the automated feedback to focus solely on argument quality, minimizing modifications to other aspects like spelling or cohesion. The system achieved automatic essay scoring agreement levels comparable to human inter-rater agreement, while also enhancing explainability. Instructing the LLM to add argument support, such as facts and examples, proved most effective for generating substantive changes, with the LLM successfully adding true information without prior background knowledge.

Key takeaway

For educators and NLP engineers developing automated writing feedback systems, this research suggests that LLMs can effectively provide targeted, non-superficial improvements to argumentative essays. You should focus on instructing the LLM to add specific argument support, such as facts and examples, to achieve meaningful changes in argument organization and development, rather than broad stylistic edits.

Key insights

LLMs can provide targeted, explainable feedback on argumentative essay quality by reformulating text.

Principles

• LLMs can score argument quality.
• Constrain LLM feedback to specific criteria.
• Adding argument support yields substantive changes.

Method

An LLM first scores argument quality (defense, organization, development), then reformulates the essay to provide feedback, specifically by adding argument support like facts and examples.

In practice

• Use LLMs for targeted essay feedback.
• Focus LLM instructions on argument support.
• Evaluate LLM scoring against human agreement.

Topics

• Automated Essay Reformulation
• Argumentative Essays
• Large Language Models
• Pedagogical Feedback
• Argument Quality Scoring

Best for: AI Scientist, NLP Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Paper Index on ACL Anthology.