# It Matters How You Say It: Exploring Rhetorical Patterns for AI-Assisted Information Evaluation

arXiv: https://arxiv.org/abs/2607.17627 · HTML: https://mlsanity.com/paper/2607.17627/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 75 references that are in the map.
- First posted on arXiv 20 Jul 2026.

## Builds on (most cited first, 18 of 18)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions](https://mlsanity.com/paper/2311.05232/) (2023)
- [Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance](https://mlsanity.com/paper/2006.14779/) (2020)
- [A Survey on Automated Fact-Checking](https://mlsanity.com/paper/2108.11896/) (2021)
- [Effect of Confidence and Explanation on Accuracy and Trust Calibration in AI-Assisted Decision Making](https://mlsanity.com/paper/2001.02114/) (2020)
- [Debating with More Persuasive LLMs Leads to More Truthful Answers](https://mlsanity.com/paper/2402.06782/) (2024)
- [Combating Misinformation in the Age of LLMs: Opportunities and Challenges](https://mlsanity.com/paper/2311.05656/) (2023)
- [Fool Me Twice: Entailment from Wikipedia Gamification](https://mlsanity.com/paper/2104.04725/) (2021)
- [Measuring and Benchmarking Large Language Models' Capabilities to Generate Persuasive Language](https://mlsanity.com/paper/2406.17753/) (2024)
- [Trends in the Diffusion of Misinformation on Social Media](https://mlsanity.com/paper/1809.05901/) (2018)
- [Supporting Self-Reflection at Scale with Large Language Models: Insights from Randomized Field Experiments in Classrooms](https://mlsanity.com/paper/2406.07571/) (2024)
- [Healthcare Copilot: Eliciting the Power of General LLMs for Medical Consultation](https://mlsanity.com/paper/2402.13408/) (2024)
- [Exploring a Behavioral Model of "Positive Friction" in Human-AI Interaction](https://mlsanity.com/paper/2402.09683/) (2024)
- [Better Slow than Sorry: Introducing Positive Friction for Reliable Dialogue Systems](https://mlsanity.com/paper/2501.17348/) (2025)
- [A Browser Extension for in-place Signaling and Assessment of Misinformation](https://mlsanity.com/paper/2403.11485/) (2024)
- [Context, Credibility, and Control: User Reflections on AI Assisted Misinformation Tools](https://mlsanity.com/paper/2506.22940/) (2025)
- [Designing Effective AI Explanations for Misinformation Detection: A Comparative Study of Content, Social, and Combined Explanations](https://mlsanity.com/paper/2509.03693/) (2025)
- [Using Grok to Avoid Personal Attacks While Correcting Misinformation on X](https://mlsanity.com/paper/2601.04251/) (2026)

## Abstract (arXiv)

Prior work on AI-assisted information evaluation has largely focused on what AI systems communicate, comparing explanation types and formats, with responses predominantly cast in directive rhetoric where the system delivers a verdict and the user passively accepts it. While debate-style interactions have recently shown promise in prompting critical evaluation over deference, the rhetorical patterns that structure AI responses and how they might induce reflection, uncertainty, or independent reasoning remain largely unexamined. To address this, we investigated eight rhetorical patterns known to induce contemplation: Intentional Misleading, Interpretive Alternative, Scaffold Explanation, Triggering Distrust, Information Distortion, Alternative Framing, Socratic Questioning, and an Oracle baseline. Through a within-subject study with n=98 participants on a hint-on-demand fact verification task, we observed preliminary evidence that Scaffold Explanation were associated with the highest accuracy gains, and encouraging deeper reflection. Surprisingly, the adversarial conditions also improved accuracy modestly. Participants preferred Alternative Framing most and Interpretive Alternative least, largely due to the latter's perceived time cost. We discuss the implications of designing conversational agents with varied rhetorical styles and the trade-offs among user performance, satisfaction, and contemplation.