# AI persuading AI vs AI persuading Humans: LLMs' Differential Effectiveness in Promoting Pro-Environmental Behavior

arXiv: https://arxiv.org/abs/2503.02067 · HTML: https://mlsanity.com/paper/2503.02067/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 55 references that are in the map.
- First posted on arXiv 3 Mar 2025.

## Builds on (most cited first, 8 of 8)

- [Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings](https://mlsanity.com/paper/1607.06520/) (2016)
- [Semantics derived automatically from language corpora contain human-like biases](https://mlsanity.com/paper/1608.07187/) (2016)
- [Out of One, Many: Using Language Models to Simulate Human Samples](https://mlsanity.com/paper/2209.06899/) (2022)
- [Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints](https://mlsanity.com/paper/1707.09457/) (2017)
- ["Kelly is a Warm Person, Joseph is a Role Model": Gender Biases in LLM-Generated Reference Letters](https://mlsanity.com/paper/2310.09219/) (2023)
- [Do LLMs exhibit human-like response biases? A case study in survey design](https://mlsanity.com/paper/2311.04076/) (2023)
- [Unraveling the Hidden Environmental Impacts of AI Solutions for Environment](https://mlsanity.com/paper/2110.11822/) (2021)
- [Future You: A Conversation with an AI-Generated Future Self Reduces Anxiety, Negative Emotions, and Increases Future Self-Continuity](https://mlsanity.com/paper/2405.12514/) (2024)

## Built on by (most cited first, 4 of 4)

- [Persuasion Propagation: Does Persuasion Change AI Agent Behavior?](https://mlsanity.com/paper/2602.00851/) (2026)
- [Addressing Climate Action Misperceptions with Generative AI](https://mlsanity.com/paper/2602.22564/) (2026)
- [Self++: Co-Determined Agency for Human--AI Symbiosis in Extended Reality](https://mlsanity.com/paper/2603.28306/) (2026)
- [LLM-generated personalized nudges for improving pro-environmental behavior: Field evidence from resource conservation](https://mlsanity.com/paper/2604.03881/) (2026)

## Abstract (arXiv)

Pro-environmental behavior (PEB) is vital to combat climate change, yet turning awareness into intention and action remains elusive. We explore large language models (LLMs) as tools to promote PEB, comparing their impact across 3,200 participants: real humans (n=1,200), simulated humans based on actual participant data (n=1,200), and fully synthetic personas (n=1,200). All three participant groups faced personalized or standard chatbots, or static statements, employing four persuasion strategies (moral foundations, future self-continuity, action orientation, or "freestyle" chosen by the LLM). Results reveal a "synthetic persuasion paradox": synthetic and simulated agents significantly affect their post-intervention PEB stance, while human responses barely shift. Simulated participants better approximate human trends but still overestimate effects. This disconnect underscores LLM's potential for pre-evaluating PEB interventions but warns of its limits in predicting real-world behavior. We call for refined synthetic modeling and sustained and extended human trials to align conversational AI's promise with tangible sustainability outcomes.