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This study investigates how eco-feedback interfaces can influence university students' usage of Large Language Models (LLMs) by visualizing latency-carbon trade-offs during interactions. The findings reveal that students' likelihood of choosing the eco-feedback system declines significantly with increased perceived response latency, while awareness of carbon-saving impacts enhances their willingness to adopt sustainable practices. Additionally, students with stronger eco-mindedness show a higher propensity for lower-carbon options, although this effect wanes with latency, underscoring the potential of eco-feedback as a sustainability intervention in educational contexts.
Eco-feedback interfaces can significantly shift university students' LLM usage towards sustainability, but only if latency is kept in check.
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.
