AI is an energy hog. This is what it means for climate change. | MIT Technology Review

The article discusses the growing concern over AI's increasing electricity demand and its implications for climate change. As AI becomes more prevalent, its energy consumption is rising, leading to questions about its impact on the electrical grid and overall carbon emissions. AI tasks, particularly those involving generative models like image creation, can be energy-intensive. For instance, generating 1,000 images with a model like Stable Diffusion XL can produce as much CO2 as driving a gas-powered car for four miles. However, other AI tasks, such as text generation, are significantly less energy-consuming.

Projections from the International Energy Agency (IEA) indicate that electricity consumption from AI, data centers, and cryptocurrency could double by 2026, potentially adding between 160 to 590 terawatt-hours (TWh) to the global electricity demand. This increase, while substantial, is part of a broader trend of rising electricity demand, driven also by electric vehicles and industrial growth.

Despite efforts by tech companies like Microsoft to achieve zero greenhouse-gas emissions, AI-related activities have complicated these goals. Microsoft's emissions have continued to rise, partly due to the infrastructure growth required to support AI technologies. Building new data centers, which involves carbon-intensive materials like steel and cement, contributes significantly to these emissions.

Historical concerns about information technology's energy demands have often been overstated, with past predictions not fully materializing. The current concern over AI's electricity use might follow a similar trajectory, with actual impacts potentially being less severe than feared.

The article concludes that the critical issue is not the rising demand itself but how this demand is met. If met with fossil fuels, it could worsen climate impacts. However, if it spurs greater investment in renewable energy and efficiency improvements in AI, it could drive a positive shift towards a cleaner energy grid.

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King George School named Inclusive School of the Year

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Local teachers have had enough of daily violence at schools, unions say

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Ontario public schools enrolment - Dataset - Ontario Data Catalogue

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5 questions to ask when evaluating AI edtech tools - SmartBrief

Summary of "5 Questions to Ask When Evaluating AI Edtech Tools" by Tracy A. Huebner and Rachel Burstein
Introduction:
The article addresses the rise of AI in education, emphasizing the need for thoughtful integration of AI tools in educational settings. It notes the initial hype and subsequent realistic adjustments in AI application within schools.

Main Argument:
The focus should shift from what AI could do to how educators can harness AI for immediate educational benefits. Effective use of AI should empower quality instruction and support teachers in key areas.

Five Critical Questions for Evaluating AI Edtech Tools:

Differentiation:

AI tools should aid in personalizing learning experiences, offering scaffolding, immediate feedback, and freeing up teacher time.
Example: Khan Academy’s chatbot, Khanmigo.
Access to Vetted Materials:

Tools should provide curated, vetted educational resources, enhancing learner variability and relevance.
Example: ISTE + ASCD’s walled garden chatbot.
Promoting Student Interest:

AI tools should facilitate student engagement by making research and learning materials accessible and interesting.
Example: Intelligent tutoring systems guiding research and source evaluation.
Opportunities for Relationship-Building:

Tools should help build strong teacher-student and teacher-family relationships, potentially through features like automatic translation and emotional check-ins.
Example: AI-powered communication tools like AllHere.
Learning Analytics:

Effective tools should provide actionable data that can guide instructional decisions and interventions.
Example: TeachFX for teacher feedback.
Conclusion:
While AI adds complexity to the edtech landscape, using the framework of tech-enabled instruction and these five questions can help educators select impactful AI tools.

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Edtech Pilot Framework - A process to help run successful ed-tech pilots

"The Edtech Pilot Framework provides a step-by-step process to help education leaders and technology developers run successful educational technology (edtech) pilots."

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Generative AI in K-12 Education Guidance | College of Education

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