Most participants expressed reluctance to adopt an eco-feedback LLM for regular use, with nearly two-thirds reporting some degree of unwillingness¶

Claim · All claims
Evidence · 1 study · design r2 · q2
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q2 i? 48.3% of participants indicated being somewhat unlikely and 16.9% extremely unlikely to adopt the system for regular use; only 5.6% reported being very likely. → Hyeonwook Kim 2026

Evidence¶

Hyeonwook Kim 2026¶

Hyeonwook Kim, Xuesi Chen, Alex Cabral, Cindy Kaiying Lin, Udit Gupta, Josiah Hester. (2026). When LLMs Slow Down: How Environmental Impacts Mediate University Students' LLM Usage. https://arxiv.org/abs/2608.23968 doi:10.48550/arxiv.2608.23968

q2 · i? · design · r2

Descriptive survey item in which 89 participants rated willingness to use an eco-feedback LLM on a 5-point Likert scale. "48.3% indicated being somewhat unlikely and 16.9% extremely unlikely to adopt the system for regular use"; 5.6% were very likely and 16.9% somewhat likely.

"48.3% indicated being somewhat unlikely and 16.9% extremely unlikely to adopt the system for regular use. These responses show that a majority of participants expressed reluctance toward adoption, with nearly two-thirds reporting some degree of unwillingness."

Discussion¶

Related Claims¶

• Concrete, outcome-oriented eco-feedback visualizations with real-world analogies are rated more favorably than abstract metrics — related
• Interacting with an eco-feedback LLM interface surfaces gaps in students' understanding of digital sustainability and supports learning about the environmental impact of everyday LLM use — related
• Stronger eco-mindedness raises initial eco-mode preference, but this advantage diminishes as response latency increases — related
• Preference for LLMs aligns only weakly with AI Interaction Competence, producing enthusiastic-but-unskilled and skilled-but-reluctant subgroups — related