From Perception to Action: Can UI Interventions Foster Sustainable LLM Chatbot

2026-06-09

AI Summary

This study addresses the high energy consumption of large language model (LLM) chatbots by investigating the underexplored role of user interface (UI) design in promoting sustainable user behavior. While existing energy-saving strategies primarily target models and infrastructure, this work proposes a UI-level intervention that integrates persuasive technology and choice architecture. The design features interactive mechanisms—including a three-mode toggle (Eco/Balanced/Performance), real-time energy feedback, predictive prompts, a sustainability dashboard, and analogical cues—to nudge users toward energy-efficient choices. Experimental results demonstrate that, in non-high-precision scenarios, 90.9% of users voluntarily selected the Eco mode and 55.8% adopted the suggested prompts, significantly enhancing energy-saving behaviors without reducing interaction length. These findings validate the effectiveness and feasibility of sustainability-oriented UI design in LLM-powered systems.

Abstract

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.

Problem

• sustainability
• LLM chatbot
• energy awareness
• user interface
• energy consumption

Innovation

• sustainable AI
• UI interventions
• energy-aware interaction
• LLM chatbots
• behavioral nudges