从感知到行动：UI干预能否促进可持续的LLM聊天机器人
From Perception to Action: Can UI Interventions Foster Sustainable LLM Chatbot
AI总结：
研究通过UI干预（如模式切换、能耗反馈）提升用户对LLM聊天机器人能耗的感知，并鼓励节能行为，发现模式切换是主要行为机制。
摘要：
基于LLM的聊天机器人日益融入日常工作流程，其能源使用引发了可持续性担忧。大多数缓解策略强调模型或基础设施效率，而用户界面层尽管具有塑造交互行为的潜力，却仍未得到充分探索。我们调查了面向可持续性的UI干预能否提高用户的能源意识，并鼓励更节能的聊天机器人使用，同时不降低可用性。我们首先进行了一项基线调查，有77名参与者评估了对干预概念的意识和接受度。在说服技术和选择架构的先前工作指导下，我们实现了一个基于Web的聊天机器人原型，具有三模式开关（节能、平衡、性能）、每次响应的能耗反馈、发送前能耗估计、使用指标仪表板和能耗类比。然后，我们在为期五天的实地研究中评估了该原型，有11名参与者。在基线调查中，94.8%的受访者报告至少对AI能耗有一定了解，但88.3%的人错误估计了实际消耗。尽管对环境影响的担忧很高，但只有39.0%的人表示愿意接受性能权衡以降低能耗。在实地研究中，节能模式占记录提示的55.8%，而90.9%的人自我报告在不需要高精度时主动选择Eco模式。参与者没有减少提示长度，表明模式切换是主要行为机制。面向可持续性的UI干预可以提高意识，并支持LLM聊天机器人中更节能的交互模式。这些效应最好被解释为行为和基于模型的估计，补充了后端效率工作，所提供的原型和复制包支持对能源感知对话式AI设计的进一步研究。
英文摘要：
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.
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