IntelliProof: An Argumentation Network-based Conversational Helper for Organized Reflection

AI summary

Overview

• Research area: Natural Language Processing (cs.CL), specifically computational argumentation, automated writing analysis, and LLM-based educational tools.
• Technical level: Intermediate — the paper describes a deployed system combining LLM prompting with a mathematical graph-scoring formula, but assumes some familiarity with LLMs, argument mining, and graph representations.
• Scope: The paper presents IntelliProof, an interactive LLM-powered system that converts argumentative essays into argumentation graphs, scores and classifies the relations within them, and exposes the structure to users through reports, a chatbot, assumption generation, and critique tools.

What This Paper Is About

Argumentative writing requires balancing claims, evidence, and counterarguments in a coherent structure, and traditional analysis methods — from rule-based systems to neural encoders — often fail to capture the nuanced relations between claims and evidence. The authors build IntelliProof to model an essay as an argumentation graph in which claims are nodes, evidence is attached as node properties, and weighted edges encode support or attack relations. The goal is not merely to score an essay automatically, but to make the reasoning behind that analysis visible and inspectable by the user, with human override available at each step.

Key Contributions

1. A graph-based representation of argumentative essays. Claims become nodes, supporting evidence is attached as node properties with a quantified strength, and weighted edges denote support or attack relations between claims, following the general approach of modeling arguments as graphs (Saveleva et al. 2021).
2. A claim credibility score grounded in a formula. The system combines evidence scores and incoming edge scores into a score $S_t = \tanh(\Delta \frac{1}{n}\Sigma_{i=0}^{n}f_E(e_i) + \Sigma_{j=0}^{m}f_{ED}(k_j) * S_{t-1})$, where $f_E$ reflects the LLM's assessment of claim support from an evidence item, $f_{ED}$ reflects support from an incoming edge, and $\Delta$ is a tunable hyperparameter.
3. Reflective tooling on top of the graph. IntelliProof generates an eight-section report (combining evidence evaluation, edge validation, assumptions analysis, and graph critique), offers a context-aware AI copilot chat interface, generates three implicit assumptions per claim relationship with importance ratings and justifications, and critiques the argument against a built-in Argument Patterns Bank.
4. A modular, working implementation. The frontend uses Vite and React.js, the backend is a Python server with FastAPI and SupaBase (PostgreSQL), and GPT-4o is accessed via OpenAI's Python library, with a design that allows substitution of other locally or remotely deployed LLMs.

Main Findings

• Claims are classified into three types: Fact, Policy, or Value, and users establish connections between claims through the main GUI.
• Evidence handling is LLM-assisted: Users upload supporting documents in PDF or image format; a dedicated LLM instance processes these files and suggests relevant text or image extracts for a specific claim, which the user attaches via drag-and-drop. Any number of supporting or negating evidence pieces can be associated with a single claim.
• Scores are derived from qualitative judgments: Because LLMs are weak at directly generating scores (Schroeder and Wood-Doughty 2025; Cui 2025), the system first generates a qualitative classification as the LLM's assessment, then uses the Evans coefficient interpretation (Evans 1996) to convert that assessment into numerical scores.
• Reports are eight sections long: Automatic report generation combines evidence evaluation, edge validation, assumptions analysis, and graph critique into a single unified report, processing graph structure, evidence quality, relationship strengths, and logical patterns simultaneously.
• Assumption generation produces three implicit assumptions: For each analysis, the system identifies three implicit assumptions that would strengthen support between claims, along with hidden premises and bridge assumptions, each with an importance rating and a justification, generated through a few-shot learning approach (Brown et al. 2020) prepared by an argumentation field expert.
• Critique targets named fallacy types: Using GPT-4o to match the overall argument against the Argument Patterns Bank — a built-in YAML database developed by an argumentation expert containing patterns for logical fallacies, good arguments, and absurd reasoning — the system identifies issues such as circular reasoning, straw man arguments, and false causes.
• GPT-4o was chosen for the demo instance based on its performance (Shahriar et al. 2024), as well as its balance of performance, cost, and availability.
• No quantitative evaluation is reported: The paper does not report benchmark results, accuracy figures, dataset sizes, or user studies for IntelliProof; the authors describe it as a robust, functional system that demonstrates feasibility.

Methodology in Plain English

The authors take an essay and break it into claims, which become nodes in a graph. Each claim is labeled as a Fact, Policy, or Value claim. Users then attach evidence — PDFs or images — and an LLM reads those files and proposes relevant passages or extracts to attach to the claim, an association the user performs by drag-and-drop.

Once evidence and edges are in place, an LLM judges how strongly each piece of evidence supports its claim and whether each edge supports or attacks the claim it connects to. Rather than asking the model for a number directly, which the authors note is a weak point for LLMs, the system asks for a qualitative classification and then converts that classification into a numerical value using the Evans coefficient interpretation. Those numbers feed into a formula that produces a claim credibility score, combining an average evidence term with edge terms weighted by the prior claim score, passed through a tanh function and scaled by a tunable parameter.

The system then reasons over the completed graph to produce an eight-section written report, answer natural-language questions through a chatbot whose context window updates in real time as the argument is built, propose three implicit assumptions that would strengthen each claim relationship, and check the argument against a curated database of argument patterns to flag fallacies. The whole thing runs as a web application with a React frontend, a FastAPI backend, SupaBase for storage, and GPT-4o as the model, deliberately kept modular so the model can be swapped.

Why This Matters

• Impact on research: The paper positions itself against automated essay scoring systems that optimize predictive accuracy, arguing that few address the interpretability of argumentative quality. By grounding feedback in explicit argument graphs and quantitative measures of coherence, it contributes to discussion of how to integrate LLMs into workflows demanding interpretability, reliability, and pedagogical value at once. It also extends prior work on interactive systems that combine generative models with human input to construct argument graphs (Lenz and Bergmann 2025).
• Real-world applications:
  • Writing instruction and tutoring, where students get structured feedback on how well their claims are supported.
  • Essay review and peer feedback, where a graph makes an argument's weak links visible at a glance.
  • Debate and forensics preparation, where assumption generation helps surface hidden premises and gaps to fill.
  • Argumentation research and annotation, where the graph structure provides an organized representation for analysis.
• Industry relevance: The system is a working web application integrating LLM APIs, a React frontend, a FastAPI backend, and PostgreSQL, which makes it a concrete template for interpretable AI products rather than a purely predictive one. The authors also note that surveys of persuasive applications highlight both the promise and the ethical risks of LLM-driven reasoning systems (Rogiers et al. 2024), and frame explicit argument graphs as a step toward safer AI in educational settings.

Future Directions

• Formal evaluation. The paper reports no benchmark results or user studies, so measuring whether the graph-based scores and generated critiques actually improve writing or align with human judgment is an open question.
• Expanding the system. The authors state the system may be expanded further in the future, though they do not specify concrete extensions.
• Model substitution. The modular design allows GPT-4o to be replaced with other locally or remotely deployed LLMs, leaving open how the scoring formula and qualitative-to-numeric conversion behave with different models.
• Broader deployment in educational settings. The paper frames safer AI deployment in education as a motivation, but does not report classroom use, teacher feedback, or study of how students interact with the graph and reports.

Target Audience

Researchers and practitioners in computational argumentation and NLP who are interested in LLM-based argument mining and graph representations of arguments; educators and educational technology developers looking for interpretable alternatives to opaque automated essay scoring; and system builders who want a concrete example of combining LLM prompting, human oversight, and structured visualization in a deployed web application. Readers seeking empirical benchmark comparisons will not find them in this paper, since no quantitative evaluation is reported.

Authors’ 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.