MAGIC: Multi-Agent Argumentation and Grammar Integrated Critiquer
Joaquín Jordán, Xavier Yin, Melissa Fabros, Gireeja Ranade, Narges Norouzi
Abstract
Automated Essay Scoring (AES) and Automatic Essay Feedback (AEF) systems aim to reduce the workload of human raters in educational assessment. However, most existing systems prioritize numeric scoring accuracy over feedback quality and are primarily evaluated on pre-secondary school level writing. This paper presents Multi-Agent Argumentation and Grammar Integrated Critiquer (MAGIC), a framework using five specialized agents to evaluate prompt adherence, persuasiveness, organization, vocabulary, and grammar for both holistic scoring and detailed feedback generation. To support evaluation at the college level, we collated a dataset of Graduate Record Examination (GRE) practice essays with expert-evaluated scores and feedback. MAGIC achieves substantial to near-perfect scoring agreement with humans on the GRE data, outperforming baseline LLM models while providing enhanced interpretability through its multi-agent approach. We also compare MAGIC
BibTeX
@inproceedings{aaai2026_magicmultiagenta,
title = {MAGIC: Multi-Agent Argumentation and Grammar Integrated Critiquer},
author = {Joaquín Jordán and Xavier Yin and Melissa Fabros and Gireeja Ranade and Narges Norouzi},
booktitle = {AAAI 2026},
year = {2026}
}