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Greetings Team, AI is no longer optional—it’s everywhere. Whether people realize it or not, they’re using AI every day, and students are no exception. From homework help to full-on assignment generation, AI is now a permanent part of the learning landscape. So what does this mean for educators? We break down practical tools and strategies for teaching in the age of AI. Meanwhile, powering this AI revolution is becoming a massive challenge. Can nuclear reactors solve the data center energy crisis? Bill Gates and Nvidia are betting on it. And is Meta trying to catch up and resorting to all types of gimmicks to do so? Let’s dive in—and stay curious.

• News and Trends.

• The 2025 Teacher’s Guide To Maximizing AI In Education

• Is Meta Behind in the AI race and desperate?

• AI Tools - GEO Tools

• Nvidia Backs Nuclear Power to Fuel AI Growth

📰 AI News and Trends

• Sam Altman Says GPT-5 Coming this Summer, Open to Ads on ChatGPT-1

• An island in the Philippines, bought by the AI chatbot company Sensay, has created a council of 17 AIs modeled on famed historical leaders to serve as the island’s government

• Scale AI’s Top Rival, Surge AI, Hits More Than $1 Billion in Sales

• Meta tried to acquire Safe Superintelligence, launched by OpenAI co-founder Ilya Sutskever, who turned him down. Mark Zuckerberg moved to recruit the CEO

• Midjourney launches its first AI video generation model, V1

🌐 Other Tech news

• A SpaceX rocket exploded on the test stand, sending a giant fireball into the Texas sky, marking the 4th incident of its kind this year.

• The $10 billion Chinese courier built on Shein and TikTok orders outcompetes Amazon across Southeast Asia.

• Tariffs and scrutiny in the US and Europe have led Chinese consumer brands to bet on becoming household names in Latin America

• X CEO Linda Yaccarino said that users of the app would soon be able to make investments or trades on X

• Microsoft Plans to Lay Off Thousands of Employees, Primarily in Sales

• FDA approves powerful HIV drug that nearly eliminated the spread in clinical trials

Is Meta Behind in the AI race and desperate?

Meta is aggressively ramping up its AI efforts by attempting to hire elite talent and invest in top startups:

• Hiring Targets: Meta plans to hire AI investors Nat Friedman and Daniel Gross, and partially acquire their venture fund NFDG, which backs firms like Perplexity, Character.AI, and Safe Superintelligence.

• Failed Acquisition: Meta tried to buy Ilya Sutskever’s $32B startup Safe Superintelligence but was rejected. They pivoted to recruit the company’s CEO instead.

• Major Investments: Meta recently invested $14.3B in Scale AI, hiring its founder Alexandr Wang and several engineers.

• Recruitment Tactics: CEO Mark Zuckerberg is personally recruiting talent with reported $ 100 M+ signing bonuses. OpenAI CEO Sam Altman confirmed Meta tried poaching their team with massive offers.

• Team Growth: Meta is building a 50-person AGI team, including hiring a chief scientist, and reorganizing its generative AI division for speed and impact.

Why Meta Is So Desperate?

Meta has lost ground to OpenAI, Google, and startups like Anthropic and DeepSeek in large language models (LLMs) and AGI progress. They had to postpone major AI launches and have lost key talent in recent years. And now Zuckerberg is in “founder mode,” personally driving the AI push to catch up and reshape Meta’s future around AI.

Meta wants to own foundational AI talent and infrastructure rather than just using external tools, and by recruiting founders and investors, it aims to absorb both innovation and access to early-stage startups.
Meta is betting tens of billions to stay competitive in AI. The moves are bold but signal urgency, and possibly, past missteps. Zuckerberg is making it clear that Meta wants to win the AI race.

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The 2025 Teacher’s Guide To Maximizing AI In Education

In just a few years, AI has gone from classroom novelty to everyday teaching ally. U.S. teachers currently work ~50 h/week—nearly half on non-instructional tasks like grading, planning, and emails. AI tools now save 5–10 hours weekly by automating these chores, enabling educators to focus on connection, creativity, and student growth. From adaptive learning platforms to AI tutors, this guide outlines practical, ethical, and impactful AI use across K–12 settings.

Why AI Matters

U.S. teachers work 50 h/week—with ~25 h on admin & grading tasks. AI can reclaim 5–10 hours weekly via automation and smart assistance.

1. Grading & Feedback

• Eduaide.AI: AI workspace built for teachers; generates feedback, lesson plans, IEP outlines, and emails (techlearning.com, eduaide.ai)

• Gradescope: Semi-automated grading for exams, code, homework; used by 25,000+ educators

• Quizizz AI: Adaptive quizzes that tailor questions based on performance (enrollify.org)

How to use: upload rubrics and student work → review AI suggestions → add personal comments to keep it human.

2. Lesson Planning

• Prompt examples:

  • “Create a middle‑school lesson on photosynthesis with 3 activities.”

  • “Generate discussion questions for To Kill a Mockingbird (9th grade).”

  • “Design a 4th‑grade fractions worksheet.”

AI turns tasks that typically take 2 h into ~30 min of prep. Tools like Eduaide also offer built-in editors and ready-to-customize outlines (eduinterface.weebly.com, techlearning.com).

3. Personalized Learning

Adaptive platforms auto-calibrate based on student performance:

• Khan Academy + Khanmigo (GPT‑4 assistant) — offers tutoring, rubric generation, and standards-aligned lesson tools (khanmigo.ai)

• [DreamBox] and [IXL]

AI Tutors (e.g., Socratic by Google) guide learners step-by-step outside the classroom .

Ethics & Trust Considerations

• Data Privacy: Ensure AI tools follow FERPA and limit data collection.

• Accuracy: Teachers must vet AI-generated content; teach students fact‑checking.

• Over-dependence: AI should support—not replace—independent thinking.

• Access Equity: Ensure all students have the devices and connectivity.

• Transparency: Communicate how AI is used to families and include oversight steps.

Teaching Students About AI

• Basic Concepts: algorithm, machine learning, training data, bias.

• Activities: Compare AI vs. human-generated summaries; test spell-check suggestions; sort image-recognition results.

• Usage Guidelines: Label work with AI help, verify outputs, set boundaries for assignments.

• Discussion Topics: When is AI helpful versus harmful? How to judge reliability?

Implementation Roadmap

1. Audit Resources: device availability, internet speed, time-draining tasks.

2. Train Teachers: start simple → hands-on projects → peer-led support.

3. Monitor & Adjust: track time saved, engagement, teacher sentiment; pivot as needed.

Final Take

AI can't replace teachers—but it can elevate them. Automating routine tasks frees educators to focus on mentorship, complex instruction, and relationship-building. The sweet spot blends AI efficiency with human empathy and judgment. Staying updated on AI trends ensures smart, ethical adoption—an essential read for any school leader or classroom innovator.

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Nvidia Backs Nuclear Power to Fuel AI Growth

Nvidia has joined Bill Gates and HD Hyundai in a $650M investment round for TerraPower, a nuclear energy startup developing small modular reactors (SMRs) to power data centers. TerraPower’s flagship project—a 345-megawatt Natrium plant in Wyoming—uses liquid sodium cooling and molten salt storage to deliver up to 1 gigawatt of stored heat. It's part of the U.S. Department of Energy's Advanced Reactor Demonstration Program.

Other tech giants are also racing to secure nuclear energy:

• Oracle: Building 3 SMRs for 1GW output

• Microsoft: Plans to restart the Three Mile Island reactor

• Google: Partnered with Kairos for 7 SMRs

• Amazon: Investing in 3 energy startups, including 2 nuclear-focused

• Meta: Entered the nuclear space only recently

AI data centers are power-hungry. For example, zettascale supercomputers may need 500MW, enough to power 375,000 homes. The current grid can’t keep up, so tech firms are turning to nuclear SMRs for long-term, reliable power.

Nvidia’s move signals that power, not GPUs, is the next big AI bottleneck—and solving it means betting on next-gen nuclear tech.

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