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

• URL: http://arxiv.org/abs/2608.23968v1
• Date: Tue, 25 Aug 2026 01:46:54 GMT
• Title: When LLMs Slow Down: How Environmental Impacts Mediate University Students' LLM Usage
• Abstract summary: We design and deploy an interface that visualizes latency-carbon trade-offs during live Large Language Models interactions.<n>We find that the likelihood of choosing the eco-feedback system significantly decreased as perceived response latency increased.<n>Students with stronger eco-mindedness demonstrated higher baseline willingness to adopt lower-carbon modes.
• Score: 2.519567577955041
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Informal Learning Emerges in Everyday Human-LLM Interaction [74.53081471113929]
  We show that everyday human-LLM interaction contains measurable, selective and conditionally organized behavioural signatures of informal learning.<n>They shift AI evaluation from answer-delivery efficiency toward the preservation of cognitive opportunities for users to reason, test ideas and construct understanding.
  arXiv  Detail & Related papers  (2026-07-20T07:56:41Z)
• Greener Than Humans? Environmental Attitudes in Large Language Models [0.21748200848556343]
  Large language models (LLMs) are increasingly used in sustainability-related decision support, reporting, and public communication.<n>This paper develops a benchmark for evaluating environmental cognition, affect, behavioural and recommendations in LLMs.
  arXiv  Detail & Related papers  (2026-06-01T18:05:53Z)
• Eco-Bee: A Personalised Multi-Modal Agent for Advancing Student Climate Awareness and Sustainable Behaviour in Campus Ecosystems [2.924756839755417]
  Eco-Bee is a conversational agent that connects individual choices to environmental limits.<n> Tailored for students at the cusp of lifelong habits, Eco-Bee delivers actionable insights, peer benchmarking, and gamified challenges.<n>In a pilot tested across multiple campus networks, 96% of the student participants supported a campus-wide rollout and reported a clearer understanding of how daily behaviours collectively impact the planet's limits.
  arXiv  Detail & Related papers  (2026-03-06T11:55:40Z)
• Meltdown: Bridging the Perception Gap in Sustainable Food Behaviors Through Immersive VR [1.335888029640176]
  We developed Meltdown, an immersive virtual reality (VR) escape room that simulates a grocery shopping and food waste management experience.<n>The game emphasizes sustainable food choices and disposal practices, combining interactive elements and narrative feedback to promote behavioral change.<n>We observed statistically significant improvements in participants objective knowledge, perceived confidence, and intention to adopt sustainable behaviors.
  arXiv  Detail & Related papers  (2025-04-19T15:26:48Z)
• From Efficiency Gains to Rebound Effects: The Problem of Jevons' Paradox in AI's Polarized Environmental Debate [69.05573887799203]
  We argue that understanding these second-order impacts requires an interdisciplinary approach, combining lifecycle assessments with socio-economic analyses.<n>We contend that a narrow focus on direct emissions misrepresents AI's true climate footprint, limiting the scope for meaningful interventions.
  arXiv  Detail & Related papers  (2025-01-27T22:45:06Z)
• Addressing the sustainable AI trilemma: a case study on LLM agents and RAG [7.6212949300713015]
  Large language models (LLMs) have demonstrated significant capabilities, but their widespread deployment and more advanced applications raise critical sustainability challenges.<n>We propose the concept of the Sustainable AI Trilemma, highlighting the tensions between AI capability, digital equity, and environmental sustainability.
  arXiv  Detail & Related papers  (2025-01-14T17:21:16Z)
• A Survey of Sustainability in Large Language Models: Applications, Economics, and Challenges [0.7889270818022226]
  Large Language Models (LLMs) have transformed numerous domains by providing advanced capabilities in natural language understanding, generation, and reasoning.<n>Despite their groundbreaking applications across industries such as research, healthcare, and creative media, their rapid adoption raises critical concerns regarding sustainability.<n>This survey paper examines the environmental, economic, and computational challenges associated with LLMs, focusing on energy consumption, carbon emissions, and resource utilization in data centers.
  arXiv  Detail & Related papers  (2024-12-06T05:20:04Z)
• Persuasion with Large Language Models: a Survey [49.86930318312291]
  Large Language Models (LLMs) have created new disruptive possibilities for persuasive communication. In areas such as politics, marketing, public health, e-commerce, and charitable giving, such LLM Systems have already achieved human-level or even super-human persuasiveness. Our survey suggests that the current and future potential of LLM-based persuasion poses profound ethical and societal risks.
  arXiv  Detail & Related papers  (2024-11-11T10:05:52Z)
• A Comparative Study of Machine Learning Algorithms for Anomaly Detection in Industrial Environments: Performance and Environmental Impact [62.997667081978825]
  This study seeks to address the demands of high-performance machine learning models with environmental sustainability. Traditional machine learning algorithms, such as Decision Trees and Random Forests, demonstrate robust efficiency and performance. However, superior outcomes were obtained with optimised configurations, albeit with a commensurate increase in resource consumption.
  arXiv  Detail & Related papers  (2023-07-01T15:18:00Z)
• Disadvantaged students increase their academic performance through collective intelligence exposure in emergency remote learning due to COVID 19 [105.54048699217668]
  During the COVID-19 crisis, educational institutions worldwide shifted from face-to-face instruction to emergency remote teaching (ERT) modalities. We analyzed data on 7,528 undergraduate students and found that cooperative and consensus dynamics among students in discussion forums positively affect their final GPA. Using natural language processing, we show that first-year students with low academic performance during high school are exposed to more content-intensive posts in discussion forums.
  arXiv  Detail & Related papers  (2022-03-10T20:23:38Z)
• Leveraging traditional ecological knowledge in ecosystem restoration projects utilizing machine learning [77.34726150561087]
  Community engagement throughout the stages of ecosystem restoration projects could contribute to improved community well-being. We suggest that adaptive and scalable practices could incentivize interdisciplinary collaboration during all stages of ecosystemic ML restoration projects.
  arXiv  Detail & Related papers  (2020-06-22T16:17:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.