From Awareness to Action? The Impact of CO2 Emission Feedback on Student LLM Usage

Proceedings of the Extended Abstracts of the 2026 CHI Conference on Human Factors in Computing Systems 2026 Open access

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Authors: Lisa Bondo Andersen, Markus Herklotz, Ailin Liu, Moritz Goeke, Michael Jülich, Christoph A. Kern, Frauke Kreuter
DOI: 10.1145/3772363.3798840
Cited by: 1
References: 28

Abstract

Given the growing adoption of Large Language Models (LLMs), mitigating the environmental impacts of AI-user interactions becomes a critical concern. We study whether real-time feedback on resource consumption changes LLM usage patterns and reduces CO2 emissions in an educational setting. Our use case draws on a between-subject experimental design and rich AI-user interaction data collected on an AI learning platform. Our results show lower emissions and total tokens per prompt for the real-time feedback group, indicating that informational feedback can influence selected measures of students’ LLM usage.

Fields of Study & Subjects

Educational Assessment and Pedagogy Engineering Education and Pedagogy Diverse Education and Engineering Focus

Publication details

Journal / source
Proceedings of the Extended Abstracts of the 2026 CHI Conference on Human Factors in Computing Systems
Publisher
ACM
Pages
1–11
Publication date
13 April 2026