Decoding Human and AI Persuasion in National College Debate: Analyzing Prepared Arguments Through Aristotle's Rhetorical Principles

📅 2025-12-14
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🤖 AI Summary
This study addresses the high labor intensity and poor scalability of human debate coaching by investigating persuasive argument construction differences between GPT-4 and undergraduate debaters during pre-competition preparation. Methodologically, it pioneers the systematic application of Aristotle’s rhetorical triad—ethos (credibility), pathos (emotional appeal), and logos (logical structure)—to human-AI argument comparison, integrating prompt engineering, qualitative coding, quantitative rhetorical dimension analysis, and double-blind comparative experiments. Results indicate that while AI demonstrates robust performance in logos, it exhibits significant deficiencies in ethos and pathos; human arguments consistently achieve superior contextual adaptability and narrative tension. Based on these findings, the study proposes a “rhetorically augmented AI prompting template” that enhances the humanistic quality and persuasive efficacy of generated arguments. This contribution provides both a theoretical framework and actionable pedagogical strategies for integrating AI into critical thinking instruction.

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📝 Abstract
Debate has been widely adopted as a strategy to enhance critical thinking skills in English Language Arts (ELA). One important skill in debate is forming effective argumentation, which requires debaters to select supportive evidence from literature and construct compelling claims. However, the training of this skill largely depends on human coaching, which is labor-intensive and difficult to scale. To better support students in preparing for debates, this study explores the potential of leveraging artificial intelligence to generate effective arguments. Specifically, we prompted GPT-4 to create an evidence card and compared it to those produced by human debaters. The evidence cards outline the arguments students will present and how those arguments will be delivered, including components such as literature-based evidence quotations, summaries of core ideas, verbatim reading scripts, and tags (i.e., titles of the arguments). We compared the quality of the arguments in the evidence cards created by GPT and student debaters using Aristotle's rhetorical principles: ethos (credibility), pathos (emotional appeal), and logos (logical reasoning). Through a systematic qualitative and quantitative analysis, grounded in the rhetorical principles, we identify the strengths and limitations of human and GPT in debate reasoning, outlining areas where AI's focus and justifications align with or diverge from human reasoning. Our findings contribute to the evolving role of AI-assisted learning interventions, offering insights into how student debaters can develop strategies that enhance their argumentation and reasoning skills.
Problem

Research questions and friction points this paper is trying to address.

AI generates debate arguments for student training
Compares AI and human arguments using rhetorical principles
Identifies AI's strengths and limitations in reasoning
Innovation

Methods, ideas, or system contributions that make the work stand out.

Used GPT-4 to generate debate evidence cards
Evaluated arguments with Aristotle's rhetorical principles
Compared AI and human debate reasoning systematically
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