2026 ASEE Annual Conference & Exposition

The accelerating pace of artificial intelligence and data-intensive computing has its own toll on the environment. Training and operating large language models (LLMs) now require enormous computational resources, as recent studies have shown. The training phase of a single AI model can generate over 625K pounds of CO2 gas—equivalent to almost five times the total emissions produced by an average American car over its lifetime [1]! As AI adoption continues to scale, data centers already account for ~4.4 % of U.S. electricity use (in 2023), and projections indicate that share could triple by 2028[2], which is also the energy equivalent of a country like Argentina or Netherlands [3]. These realities reveal an urgent need to prepare the next generation of engineers to design, deploy, and manage computing systems through a lens of environmental responsibility.
As quoted from the famous Brundtland report (1987), “Sustainable Development is a development that meets the needs of the present without compromising the ability of future generations to meet their own needs”. This work-in-progress explores a pedagogical approach that integrates green computing and sustainable software development concepts into core Electrical and Computer Engineering (ECE) courses, namely Algorithms for Big Data and Senior Design. The project aims to move sustainability from a peripheral ethical topic to a core technical competency by developing a set of modular learning units that explicitly connect green computing, hardware utilization, and software efficiency to their environmental and societal impacts. Students are exposed to contemporary case studies—from data center cooling optimization to carbon-aware software scheduling—highlighting how computational choices translate directly into energy use. These modules, targeting juniors and seniors, will be impacting about 110 students annually in the ECE department at the school hosting this study.
The study adopts a mix of evaluation methods to assess both learning outcomes and attitudinal shifts. Quantitative evaluation will be derived from targeted assignments and performance metrics, directly related to the educational modules proposed. While qualitative data are gathered through reflective surveys to gauge the students’ eco-consciousness and sustainability-oriented technical reasoning. The authors will be addressing the following two research questions: 1) How can sustainability-oriented modules be interwoven into deeply technical elective courses without displacing core content, 2) Which evaluation strategies effectively measure changes in students’ technical understanding, eco-consciousness, and future intentions, and 3) In what ways did students’ perceptions and knowledge of green computing and sustainable software development change as a result of the learning modules?
Ultimately, this work seeks to foster engineers capable of advancing innovation while safeguarding planetary resources, by framing green computing and sustainable software development as essential engineering practice rather than a secondary concern
[1] ] https://arxiv.org/pdf/2311.16863.pdf
[2] https://iee.psu.edu/news/blog/why-ai-uses-so-much-energy-and-what-we-can-do-about-it?utm_source=chatgpt.com
[3] https://www.holisticai.com/blog/environmental-impact-ai-llms