When LLMs Slow Down: How Environmental Impacts Mediate University Students' LLM Usage

📅 2026-08-24
📈 Citations: 0
Influential: 0
📄 PDF
🤖 AI Summary
研究通过设计并部署可视化延迟-碳排放权衡的界面,探索了生态反馈界面如何影响大学生在使用大型语言模型时的可持续性意识和决策。
📝 Abstract
Large Language Models (LLMs) are increasingly being embedded into all facets of society, from search to education, industrial, and financial applications. These systems' carbon and water footprints raise important sustainability concerns, particularly with adoption rates exceeding 80% among university students, despite limited insight into the environmental impacts of individual usage. Eco-feedback interfaces offer a promising approach to encourage more sustainable behaviors, yet their role in shaping LLM users' sustainability awareness and decision-making remains underexplored. We design and deploy the interface that visualizes latency-carbon trade-offs during live LLM interactions. We study its use with undergraduate computer science students (N=89, ages 18-24), enrolled in a computing ethics course, providing an empirical look at how a technically sophisticated and values-oriented user population responds to sustainability-aware AI interfaces. We found that the likelihood of choosing the eco-feedback system significantly decreased as perceived response latency increased (p < .001), while users' willingness increased when they recognized the carbon-saving impacts (p < .01). Also, students with stronger eco-mindedness demonstrated higher baseline willingness to adopt lower-carbon modes and reported increased awareness of the environmental impacts of LLM use, though this effect diminished as latency increased. These results position eco-feedback interfaces as a promising sustainability intervention and highlight their potential as an educational opportunity to promote more sustainable LLM use among university students and beyond.
Problem

Research questions and friction points this paper is trying to address.

Large Language Models
Sustainability
Eco-feedback Interfaces
University Students
Environmental Impact
Innovation

Methods, ideas, or system contributions that make the work stand out.

Eco-feedback interfaces
Latency-carbon trade-offs
Sustainability awareness
💼 Related Jobs
No related jobs found.