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, few report computational resources and almost none discuss environmental impacts as an ethical concern. The research highlights a gap in standardized procedures for measuring and reporting the computational and environmental costs of LLMs in educational settings. The findings underscore the need for transparency about hidden costs as LLM adoption in education accelerates.
Researchers conducted a systematic review of papers presented at the AIED 2025 conference to assess how computational and environmental costs of large language models are reported in educational AI applications. The study found that while LLMs are widely adopted in the AIED community, standardized measurement and reporting practices are largely absent. In response, the authors propose an open-source methodology for systematically measuring and reporting both computational expenses and carbon footprints of LLM-based educational systems, with tools applicable to both local and cloud-based hardware. They also provide a formula for calculating computational costs of frontier LLMs when parameter counts are unavailable. The researchers argue that increased transparency about these hidden costs is essential for ethical AI development in education.
What's missing
The study does not provide specific quantitative data on the actual environmental costs or carbon emissions measured across the reviewed papers, nor does it detail the magnitude of difference between reported and unreported impacts.
What different sources said
- arXiv cs.AICenter
The Environmental Cost of LLMs in AIED: Reporting and Practices
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