IntelliProof: An Argumentation Network-based Conversational Helper for Organized Reflection
Kaveh Eskandari Miandoab, Katharine Kowalyshyn, Kabir Pamnani, Anesu Gavhera, Vasanth Sarathy, Matthias Scheutz
TL;DR
IntelliProof tackles the challenge of analyzing argumentative essays by reframing them as argumentation graphs where claims are nodes, evidence is attached as node properties, and edges encode support or attack relations. It leverages LLMs to score, classify, and justify these relations while offering interactive visualization and strong human oversight, aiming to improve interpretability and pedagogical value. Key contributions include a graph-based analysis workflow, a quantitative claim credibility score, automated report generation, assumptions and critique tooling, and an AI Copilot interface, all implemented in a modular, scalable architecture. The approach has practical implications for educational settings and safer AI-assisted argument analysis, enabling rapid exploration of argumentative quality with transparent scoring and justification.
Abstract
We present IntelliProof, an interactive system for analyzing argumentative essays through LLMs. IntelliProof structures an essay as an argumentation graph, where claims are represented as nodes, supporting evidence is attached as node properties, and edges encode supporting or attacking relations. Unlike existing automated essay scoring systems, IntelliProof emphasizes the user experience: each relation is initially classified and scored by an LLM, then visualized for enhanced understanding. The system provides justifications for classifications and produces quantitative measures for essay coherence. It enables rapid exploration of argumentative quality while retaining human oversight. In addition, IntelliProof provides a set of tools for a better understanding of an argumentative essay and its corresponding graph in natural language, bridging the gap between the structural semantics of argumentative essays and the user's understanding of a given text.