LLM聊天机器人能耗问题日益突出，这项研究为普通用户和产品设计师提供了可操作的节能方案——通过简单的UI调整就能改变使用习惯，做AI应用或关注可持续技术的团队值得一看。
一项新研究探讨了通过用户界面干预来提升LLM聊天机器人使用中的能源意识。基线调查显示，94.8%的受访者知道AI耗能，但88.3%低估了实际消耗，且仅39%愿意牺牲性能换取节能。在为期五天的实地研究中，节能模式占55.8%的提示，90.9%的参与者报告在不需要高精度时主动选择节能模式。研究表明，UI干预（如模式切换和能耗反馈）能有效促进节能行为，且不会显著降低可用性。该发现为设计更可持续的对话AI提供了新思路。
From Perception to Action: Can UI Interventions Foster Sustainable LLM Chatbot
LLM-powered chatbots are increasingly embedded in everyday workflows, raising sustainability concerns due to their energy use. Most mitigation strategies emphasize model or infrastructure efficiency, while the user-interface (UI) layer remains underexplored despite its potential to shape interaction behavior. We investigate whether sustainability-oriented UI interventions can increase users' energy awareness and encourage more energy-responsible chatbot use without reducing usability. We first conducted a baseline survey with 77 participants to assess awareness and receptiveness to intervention concepts. Guided by prior work on persuasive technology and choice architecture, we implemented a web-based chatbot prototype with a three-mode switch (Energy-efficient, Balanced, Performance), per-response energy feedback, pre-send energy estimates, a usage metrics dashboard, and energy analogies. We then evaluated the prototype in a five-day field study with 11 participants. In the baseline survey, 94.8% of respondents reported at least some awareness of AI energy use, yet 88.3% misestimated actual consumption. Although concern about environmental impact was high, only 39.0% indicated willingness to accept a performance trade-off for lower energy use. In the field study, Energy-efficient mode accounted for 55.8% of logged prompts, while 90.9% self-reported actively choosing Eco-mode when high accuracy was not required. Participants did not reduce prompt length, suggesting mode switching as the primary behavioral mechanism. Sustainability-oriented UI interventions can improve awareness and support more energy-responsible interaction patterns in LLM chatbots. These effects are best interpreted as behavioral and model-based estimates that complement backend efficiency work, and the provided prototype and replication package support further research on energy-aware conversational AI design.