FOXGLOVE: Understanding Goal-Oriented and Anchored Writing Feedback from Experts and LLMs on Argumentative Essays
arXiv:2606.06271v1 Announce Type: new Abstract: While large language models (LLMs) are increasingly used to generate writing feedback, there remains no systematic comparison of LLM and expert feedback on the dimensions that writing research identifies as central to revision: goal-orientation, anchoring to specific sentences, and prioritization. We introduce FOXGLOVE, a dataset of 696 feedback comments written by trained writing instructors on 69 twelfth-grade argumentative essays, paired with 1,644 comments generated from four frontier LLMs under a shared protocol, totaling 2,340 comments. We
The proliferation and increasing sophistication of large language models for generative tasks, including feedback, necessitates rigorous comparative analysis to understand their utility and limitations.
This study provides a systematic comparison of expert and LLM feedback on argumentative essays, offering critical insights into the capabilities and shortcomings of AI in educational and creative domains.
We now have a quantifiable dataset and framework to evaluate the quality of LLM-generated feedback against human expert feedback, specifically on goal-orientation, anchoring, and prioritization.
- · AI developers
- · Educational technology platforms
- · Students receiving feedback
- · Ineffective AI feedback systems
Refined LLM training data and methodologies for generating targeted and actionable writing feedback will emerge.
Improved AI-powered writing assistants could significantly enhance student learning outcomes and reduce instructor workload in essay-based curricula.
The democratization of high-quality writing feedback might fundamentally alter pedagogical approaches to writing instruction, potentially allowing for more personalized and scalable learning experiences.
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