Study Reveals Lack of Environmental Impact Reporting for LLMs in Educational AI Systems
A literature review of AIED 2025 conference papers found that while most projects use large language models, almost none disclose their environmental or computational costs. The study highlights a systemic lack of standardized reporting practices in the AI-in-education research community. The authors propose open-source tools and formulas to help researchers measure and transparently report the carbon footprint of LLM usage.
Researchers reviewing papers from the AIED 2025 conference found that large language models are now widely used in AI-in-education research, yet the vast majority of studies fail to report the computational resources consumed or discuss environmental impacts as an ethical concern. The authors argue that these costs are largely hidden due to the absence of standardized measurement and disclosure procedures. To address this gap, they developed an open-source framework for systematically measuring carbon footprints of both locally hosted and cloud-based machine learning systems. They also provide a formula to estimate the computational expense of frontier LLMs even when the precise number of model parameters is not publicly known. The study calls on the AIED research community to adopt more transparent reporting norms, framing environmental impact disclosure as an ethical responsibility alongside other considerations such as fairness and privacy.
What's missing
The study's own limitations include that it reviews only AIED 2025 conference proceedings, which may not be representative of broader AI-in-education or general AI research communities.
What different sources said
- arXiv cs.AICenter
The Environmental Cost of LLMs in AIED: Reporting and Practices
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