# Train Yourself as an LLM: Exploring Effects of AI Literacy on Persuasion via Role-playing LLM Training

arXiv: https://arxiv.org/abs/2604.02637 · HTML: https://mlsanity.com/paper/2604.02637/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 44 references that are in the map.
- First posted on arXiv 3 Apr 2026.

## Builds on (most cited first, 13 of 13)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [How Johnny Can Persuade LLMs to Jailbreak Them: Rethinking Persuasion to Challenge AI Safety by Humanizing LLMs](https://mlsanity.com/paper/2401.06373/) (2024)
- [Persuasion for Good: Towards a Personalized Persuasive Dialogue System for Social Good](https://mlsanity.com/paper/1906.06725/) (2019)
- [On the Conversational Persuasiveness of Large Language Models: A Randomized Controlled Trial](https://mlsanity.com/paper/2403.14380/) (2024)
- [Hidden Persuaders: LLMs' Political Leaning and Their Influence on Voters](https://mlsanity.com/paper/2410.24190/) (2024)
- [Fact-checking information from large language models can decrease headline discernment](https://mlsanity.com/paper/2308.10800/) (2023)
- [MentalManip: A Dataset For Fine-grained Analysis of Mental Manipulation in Conversations](https://mlsanity.com/paper/2405.16584/) (2024)
- [MAILS -- Meta AI Literacy Scale: Development and Testing of an AI Literacy Questionnaire Based on Well-Founded Competency Models and Psychological Change- and Meta-Competencies](https://mlsanity.com/paper/2302.09319/) (2023)
- [Labeling Messages as AI-Generated Does Not Reduce Their Persuasive Effects](https://mlsanity.com/paper/2504.09865/) (2025)
- [Resistance Against Manipulative AI: key factors and possible actions](https://mlsanity.com/paper/2404.14230/) (2024)
- [Evaluation of In-Person Counseling Strategies To Develop Physical Activity Chatbot for Women](https://mlsanity.com/paper/2107.10410/) (2021)
- [Can AI-Generated Persuasion Be Detected? Persuaficial Benchmark and AI vs. Human Linguistic Differences](https://mlsanity.com/paper/2601.04925/) (2026)
- [Unknown Unknowns: Do Hidden Intentions in LLMs Evade Detection?](https://mlsanity.com/paper/2601.18552/) (2026)

## Abstract (arXiv)

As large language models (LLMs) become increasingly persuasive, there is concern that people's opinions and decisions may be influenced across various contexts at scale. Prior mitigation (e.g., AI detectors and disclaimers) largely treats people as passive recipients of AI-generated information. To provide a more proactive intervention against persuasive AI, we introduce LLMimic, a role-play-based, interactive, gamified AI literacy tutorial, where participants assume the role of an LLM and progress through three key stages of the training pipeline (pretraining, SFT, and RLHF). We conducted a 2 × 3 between-subjects study (N = 274) where participants either (1) watched an AI history video (control) or (2) interacted with LLMimic (treatment), and then engaged in one of three realistic AI persuasion scenarios: (a) charity donation persuasion, (b) malicious money solicitation, or (c) hotel recommendation. Our results show that LLMimic significantly improved participants' AI literacy (p < .001), reduced persuasion success across scenarios (p < .05), and enhanced truthfulness and social responsibility levels (p<0.01) in the hotel scenario. These findings suggest that LLMimic offers a scalable, human-centered approach to improving AI literacy and supporting more informed interactions with persuasive AI.