AI说服AI vs AI说服人类：LLM在促进环保行为中的差异化效果
AI persuading AI vs AI persuading Humans: LLMs' Differential Effectiveness in Promoting Pro-Environmental Behavior
AI总结：
本研究比较了LLM在促进环保行为中对真实人类、模拟人类和合成角色的说服效果，发现“合成说服悖论”：AI对模拟和合成代理影响显著，但对真实人类影响微弱，警示了用AI预测现实行为的局限。
摘要：
环保行为（PEB）对应对气候变化至关重要，但将意识转化为意图和行动仍然困难。我们探索将大型语言模型（LLM）作为促进PEB的工具，比较了其在3,200名参与者中的影响：真实人类（n=1,200）、基于真实参与者数据的模拟人类（n=1,200）以及完全合成的角色（n=1,200）。所有三组参与者都面对个性化或标准聊天机器人，或静态陈述，采用四种说服策略（道德基础、未来自我连续性、行动导向或由LLM选择的“自由式”）。结果揭示了一个“合成说服悖论”：合成和模拟代理显著影响了它们干预后的PEB立场，而人类的反应几乎没有改变。模拟参与者更好地近似了人类趋势，但仍然高估了效果。这种脱节突显了LLM在预评估PEB干预方面的潜力，但也警告了其在预测现实世界行为方面的局限性。我们呼吁改进合成建模，并持续和扩展人类试验，以使对话AI的承诺与切实的可持续性结果相一致。
英文摘要：
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.
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