利用大型语言模型的迭代个性化增强行为提示：一项关于电力和热水节约的实地实验
LLM-generated personalized nudges for improving pro-environmental behavior: Field evidence from resource conservation
AI总结：
本研究利用大型语言模型实现迭代个性化，通过实地实验发现其在促进节能方面效果显著，且在前两轮干预中表现更优，但热水节约效果较弱且随时间减弱。
摘要：
提示被广泛用于促进行为改变，但其效果常受限于接收者在不断变化的情况下反复将反馈转化为可行下一步的难度。大型语言模型（LLM）可能通过生成个性化指导并在干预轮次中迭代更新来减少部分认知负担。我们开发了一个LLM代理用于迭代个性化，并在中国233名大学居民中进行了一项三臂随机实验，使用每日电力和淋浴热水节约作为客观测量案例，其摩擦程度不同。LLM个性化提示（T2）产生了最大的节约效果，而图像增强的传统提示（T1）和文本基于的传统提示（C）有相似的结果（综合p=0.009）。与C相比，T2减少了0.56千瓦时每间房天（p=0.014），对应调整后的节约率提高了18.3个百分点。这种优势出现在前两轮干预中，伴随个性化指导的迭代更新，并在之后持续存在。热水结果方向相同，但更小、估计不精确且随时间减弱，这与该领域更强的摩擦一致。LLM个性化提示强调前瞻性且情境特定的指导，并与参与者参与度更高相关。本研究提供了LLM基于迭代个性化增强行为提示的现场证据，行为摩擦可能是一个潜在的边界条件。需要更大规模的试验和扩展到更多行为。
英文摘要：
Encouraging pro-environmental behavior remains a major challenge for sustainable cities. Conventional feedback nudges can show individuals how their current behavior compares with environmental goals but often provide limited guidance on what to do differently in daily life. This study examines whether supplementing weekly feedback on participants' behavior with LLM-generated personalized action suggestions improves pro-environmental behavior, using daily electricity and hot-water conservation as a case study. We developed an LLM agent that generated weekly conservation messages from participant profiles, recent consumption records, and prior interaction history, combining a usage report with personalized suggestions, behavioral-change scenarios, and estimated savings. The agent was evaluated in a three-arm randomized field experiment with 233 university residents in Beijing from November 2024 to January 2025. Participants received text-based nudges, image-enhanced nudges, or LLM-generated personalized nudges over five intervention rounds. Daily electricity use and shower hot-water use were measured using dormitory meter readings and billing records. Compared with text-based feedback, LLM-generated personalized nudges reduced electricity consumption by 0.56 kWh per room-day (p = 0.014), corresponding to an 18.3 percentage-point higher saving rate. Image-enhanced feedback alone showed no clear improvement. Hot-water savings followed the same direction but were smaller and less precisely estimated (9.8 percentage points, p = 0.087). Personalized nudges contained more planning, appliance-specific, and action-oriented language and were associated with more sustained, task-focused engagement. These findings offer a pathway for integrating generative AI into sustainable urban management.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
