AI-Driven Argumentation Models for Structured Domain Reasoning

Abstract

AI-driven argumentation models offer a structured approach to automated reasoning by combining symbolic
logic representation, domain-grounded knowledge retrieval, and natural language articulation. This study
introduces a hybrid framework that enables machine learning systems to generate explicit, traceable argument
chains supporting domain-specific decision-making. The methodology incorporates premise–claim
scaffolding, state-tracked inference evolution, and structured counterargument generation to ensure coherence
across multi-stage analytical workflows. Evaluation results show that the proposed approach improves
interpretability and reduces unsupported inference leaps compared to conventional generative models. The
system demonstrates effectiveness in both static reasoning tasks and user-interactive deliberation, maintaining
logical consistency while enabling adaptive refinement of conclusions. These findings suggest that AI-based
reasoning architectures can meaningfully augment expert decision processes in domains requiring
transparency, justification, and multi-perspective analysis.