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When LLMs Slow Down: How Environmental Impacts Mediate University Students' LLM Usage — arXiv:2608.23968

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.23968
License (as archived): CC0-1.0
Archived: 2026-10-18T06:32:07.868Z
SHA-256 of the archived text: 35bf452c8004060ff26f72bc5f91097748a849fdf873aadde9da2b2e63db6635
Stable URL of this page: https://metraipolis.com/documento/dc_64fb2ffd1fcb

How to cite

When LLMs Slow Down: How Environmental Impacts Mediate University Students' LLM Usage — arXiv:2608.23968 — arXiv (en), https://arxiv.org/abs/2608.23968 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_64fb2ffd1fcb (Metraipolis archive dc_64fb2ffd1fcb, sha256 35bf452c8004060ff26f72bc5f91097748a849fdf873aadde9da2b2e63db6635)

BibTeX

@misc{dc64fb2ffd1fcb,
  title = {When LLMs Slow Down: How Environmental Impacts Mediate University Students' LLM Usage — arXiv:2608.23968},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.23968},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_64fb2ffd1fcb; sha256 35bf452c8004060ff26f72bc5f91097748a849fdf873aadde9da2b2e63db6635},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.23968. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-08-26 in cs.HC).
Categorie arXiv: Human-Computer Interaction (cs.HC) ; Computers and Society (cs.CY).
Pagina del lavoro: https://arxiv.org/abs/2608.23968 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-18T06:28:16.174Z dalla pagina https://arxiv.org/catchup/cs.HC/2026-08-26?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).