arXiv:2606.11215v1 Announce Type: cross Abstract: Large Language Model (LLM) usage in recent years has become increasingly widespread in the Artificial Intelligence in Education (AIED) community. While LLMs offer unique avenues for learners and educators, using LLMs comes with computational and environmental costs. These costs are mostly hidden due to a lack of standardised procedures to measure and report these impacts. To address this gap, we first conducted a literature review of all papers published as part of the AIED 2025 conference proceedings, determining if and how computational or en
The proliferation of LLMs across various applications, including AIED, is intensifying scrutiny on their often-hidden computational and environmental footprint, leading to calls for standardized reporting.
This highlights the growing, unaddressed environmental cost of AI development and deployment, which could become a significant constraint and regulatory focal point.
There will be increasing pressure on AI developers and users to measure and report the environmental impact of their models, potentially driving demand for more efficient AI architectures and greener compute infrastructure.
- · Energy-efficient AI hardware developers
- · Carbon accounting and reporting firms
- · Green computing solution providers
- · AI researchers focused on efficiency
- · Developers of highly compute-intensive, inefficient LLMs
- · Cloud providers without green energy commitments
- · Organizations ignoring their AI carbon footprint
- · AI sectors reliant on unrestrained compute
Increased reporting requirements and public awareness of AI's environmental cost will emerge.
This could lead to regulatory frameworks or carbon taxes specifically targeting AI compute, incentivizing efficiency.
Long-term, environmental concerns might steer AI development towards smaller, specialized, and more energy-efficient models, shifting the competitive landscape.
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Read at arXiv cs.AI