This was an interesting article that dealt with more than AI’s impact on the environment. See included table for what other topics. For now, I found this the most compelling.
Impact on Environment
AI is reliant on a chain of extractive processes that are resource-intensive and with deleterious planetary consequences.
In short, the growing use of AI technologies in education comes at considerable environmental cost – implicated in the depletion of scarce minerals and metals required to manufacture digital technologies, massive amounts of energy and water required to support data processing and storage, and fast-accumulating levels of toxic waste and pollution arising from the disposal of digital technology (see Brevini, 2021).
Given all the above, any enthusiasms for the increased use of AI in education must address the growing concerns among ecologically-concerned commentators that it might not be desirable (and perhaps even impossible) to justify the development and use of AI technologies in the medium to long-term. (source)
Overview of Roadblocks and Detours in Article
An AI generated list:
| Roadblocks | Detours |
|---|---|
| 1. AI cannot fully model the complexity of education | - Recognize AI’s limits in education - Make AI adapt to schools, not vice versa |
| 2. AI may cause harm to marginalized students | - Include educators and impacted groups in AI development - Build AI to prevent discrimination |
| 3. Adapting education to fit AI may lead to losses | - Keep human elements central in AI for education - AI should support, not replace, traditional teaching |
| 4. Data-heavy AI has ecological and environmental costs | - Pursue ‘green’ and sustainable AI - Question if AI in education is worth the environmental impact |
Key takeaways:
- Slow down and rethink the AI and education discussion
- Face AI’s complexities and risks head-on
- Involve educators and affected communities
- Put humans first, not technology
- Think hard about AI’s long-term sustainability in education