America’s AI Action Plan: Build It, Teach It, Lead It (But Maybe Don’t Question It?)

So here we are.

The Trump administration just dropped America’s AI Action Plan, and it's quite the read. Think "Build it, Teach it, Lead it" meets "Make America Great Again," with a side of "we're definitely not overthinking the implications of this at all."

It's bold. It's ambitious. And depending on your perspective, it's either a masterclass in strategic planning or the opening chapter of a dystopian thriller where the algorithm already has its own office, parking spot, and slightly too much decision-making power.

(Spoiler: I'm fairly confident that AI helped write this thing judging by the amount of em dashes, even if the authors are about as open about it as a teenager denying they ate the last Tim Tam, despite the evidence smeared across their face.)

As someone who's spent years helping educators navigate technological change, I found myself both impressed and thoughtfully uneasy working through this 25-page blueprint for American AI supremacy.

Through the Education Lens: Promise Wrapped in Ideological Bubble Wrap

The Good (and there's plenty)

This isn't another vague "AI is coming, maybe we should do something" whitepaper destined for digital purgatory. This plan comes with real funding, clear timelines, and actual responsibilities. Within 120 days, the U.S. Department of Education must roll out AI literacy programmes for K-12. A new White House Task Force on AI Education will coordinate efforts across national departments.

In a world where some schools are still debating whether ChatGPT is a calculator or contraband, this level of federal coordination is notable.

They're not just targeting computer science students either. The plan weaves AI into broader literacy goals, promises to upskill teachers, reimagine curricula, and expand dual-enrolment programmes. If you build educational tools, platforms, or lesson plans, competitions, incentives, and shiny new partnerships await.

There's also a refreshingly pragmatic nod to workforce realities. The plan recognises that AI is transforming jobs, not just headlines, putting actual money behind retraining displaced workers. The proposed AI Workforce Research Hub feels like evidence-based infrastructure educators have been requesting for years.

It all sounds solid. But here's what matters: Are we chasing AI as the next shiny thing, or designing with long-term purpose? If this is about headlines over real change, we risk building impressive tools that solve yesterday's problems whilst missing tomorrow's questions entirely.

The Uncomfortable Bits (Curiously Omitted)

Here's where I had to put my coffee down and reread sections out loud to confirm this wasn't satire.

The plan explicitly bans any federally funded AI programme from engaging with content related to misinformation, Diversity, Equity and Inclusion, or climate change.

Yes, you read that correctly.

Apparently, the U.S. government now considers "neutral" AI to be AI that avoids discussing systemic bias, environmental responsibility, or information accuracy itself. Your AI tutor can help you write a persuasive essay, but can't mention why some voices carry more weight than others, or why parts of the country are literally and metaphorically on fire.

When critical thinking and information literacy are supposed to be central to education, explicitly excluding key real-world contexts feels like teaching road safety whilst pretending cars don't exist.

The plan insists government AI systems must be "objective and free from top-down ideological bias." Sounds reasonable until it proceeds to define exactly what counts as ideology. It's like wanting neutral referees, then handing them a rulebook with half the pages torn out.

The Genuinely Worrying Gaps

What concerned me most wasn't what's in the document, but what's conspicuously absent.

Infrastructure vs Reality

Plenty of talk about data centres and compute power, precious little about reaching underfunded schools. If a regional school can't maintain stable Wi-Fi, how exactly will it implement sophisticated AI programmes? "Equity" appears like decorative seasoning rather than foundation ingredient.

Privacy Silence

Despite all the AI system focus, there's eerie silence on student data privacy. Confidentiality gets briefly mentioned in research contexts, but no clear strategy for protecting student information when AI tools enter everyday classrooms. Given what we know about how these systems learn and remember, this oversight feels dangerously naive.

Teacher Preparation

"Upskilling teachers" appears confidently throughout. But what does that mean? Button-clicking training, or reimagining pedagogy to truly support inquiry, critical thinking and differentiation?

If we're simply training teachers to operate tools without rethinking how and why we teach, we're not preparing for the future. We're just automating the past with fancier equipment.

The Swiss Alternative: A Different Race Entirely

Whilst the U.S. builds AI like training for high-speed sprints, Switzerland quietly assembles something far more deliberate. They've invested CHF 11 million (around AUD 19 million) creating an open, multilingual large language model built on public infrastructure, powered by renewable energy, designed to serve public good rather than corporate shareholders.

The Swiss model exemplifies what scholars and practitioners call ProSocial AI (thanks Brett Salakas for putting this on my radar), guided by four principles: AI should be Tailored, Trained, Tested and Targeted with social wellbeing at heart. The aim isn't impressing investors, but strengthening democracy, public trust and human dignity.

Unlike commercial models locked behind APIs and usage caps, the Swiss version is completely open. Code, training data and documentation are all public. It's the policy equivalent of leaving chocolate on hotel pillows: generous, precise, no strings attached.

They're not trying to win AI arms races. They're building something multilingual, ethical and sustainable that prioritises inclusion and public benefit. No corporate watermarks or privacy setting sacrifices for "free" trials. It's slow by design, like waiting for perfect cheese to mature. But it's built to last.

Where others chase headlines, Switzerland focuses on legacy. No fanfare, no flash. Just quiet competence, like reliable trains arriving exactly on time with data protection policies printed in multiple languages (yes, this thing is built for equity with numerous languages).

Frankly, in the current climate, that's starting to feel like the strategy schools can actually trust.

An Australian Perspective: Admiring the Ambition, Raising the Eyebrow

From across the Pacific, parts of this look impressive. Australia is wrestling with national coordination on AI in education too. The U.S. model offers a possible roadmap for investment and systematic rollout we could learn from.

But before hitting "Ctrl+C" on this policy and dropping it into Parliament House, there are uniquely Aussie realities to consider.

Values Mismatch

Whilst the U.S. launches AI strategies like startup pitches, and Switzerland gently engineers ethical models over fondue, Australia starts with: "Is this even appropriate?"

We've banned phones in most classrooms, are planning underage social media restrictions, and toying with age verification for search engines. If education policy were a theme park, we'd be checking bags at the gate and confiscating bubblegum.

When an AI plan arrives complete with climate and equity discussion bans, it's not just a bad cultural fit. It's like introducing TikTok dances into HSC English exams: fast, loud, and missing the point entirely.

Governance Reality Check

Unlike the U.S., where federal departments strong-arm their way into classrooms, Australia runs on collaboration and curriculum council diplomacy. Any national AI strategy here needs to pass through more gates than a school fire drill: federal policy, state education departments, and at least one overworked ACARA or TEQSA working group.

Imposing a centralised AI agenda in our system is like getting uniform socks at a public school. Technically possible, deeply optimistic.

Privacy Standards

In Australia, privacy isn't optional, especially for kids. With GDPR-style protections, a sceptical public, and moves to tighten online safety further, any AI entering schools needs more safeguards than Year 7s on camp.

If Silicon Valley's motto is "move fast and break things," ours is "move carefully and get signed consent in triplicate." Sounds slow, but when we finally bring AI into classrooms, it might actually stay there without accidentally emailing assessment histories to marketing bots. And, to be fair, the Federal Government did get the AI Framework for Education out quickly, but law and direction has been lacking since.

What This Actually Means for Educators

We're watching two fundamentally different AI policy visions emerge. The U.S. moves quickly, focusing on scale, supremacy, and market integration. Switzerland offers something more considered: ethics, equity, and public ownership.

Australia faces a choice. Race toward volume and visibility, rolling out AI tools with maximum urgency? Or move with precision, embedding tools that genuinely support curiosity, inclusion, and deep learning?

Here's what we should be asking: Are our AI tools helping students see the world more clearly, or reflecting back narrow, filtered reality? Are we building teacher capacity to adapt, explore, and transform practice, or just teaching prompt management?

It's time to stop mistaking tool training for professional development. Knowing how to use AI isn't the same as knowing how to use it wisely.

The real question isn't whether AI will transform education. It's whether we'll be intentional about what transformation we're building.

The Uncomfortable Truth We're Dancing Around

Every AI education decision is also a decision about what kind of learners we're growing. Do we want students who are efficient, compliant and good at prompts? Or students who ask hard questions, challenge easy answers and use technology as a tool, not a crutch?

The Trump plan is loud, fast and tightly controlled. The Swiss plan is quiet, deliberate and deeply collaborative. One's designed to win. The other's designed to endure.

Here's what matters: What kind of win are we chasing?

Are we building AI that helps students learn, or just makes marking easier? Are we preparing young people to thrive in the future, or merely survive the present?

Because ultimately, it's not about the tech. It's about the thinking.

If we don't get that bit right, all the dashboards, funding, and media buzz won't matter.

What kind of AI future are we actually building for the students sitting in our classrooms today?

Thoughts? Disagreements? Gentle corrections to my obviously flawed reasoning? The comments await.

Acknowledgement: Claude (Sonnet 4) was used to edit this article for clarity. Images created by ChatGPT

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