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

Source: arXiv - 2606.10861v1

Overview

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.

Key Contributions

This paper presents research in the following areas:

• cs.SE
• cs.AI
• cs.HC

Methodology

Please refer to the full paper for detailed methodology.

Practical Implications

This research contributes to the advancement of cs.SE.

Authors

• Nitish Patkar
• Pooja Rani
• Jack Glässer
• Simon Lüscher
• Martin Kropp

Paper Information

• arXiv ID: 2606.10861v1
• Categories: cs.SE, cs.AI, cs.HC
• Published: June 9, 2026
• PDF: Download PDF