Schools need AI strategy. Policies are not enough.
And why AI, used strategically, may be our best chance to rehumanize education…
Schools around the world are racing to deal with AI.
We are weaving it into curriculum units, cramming it into acceptable-use policies, and conducting training for adults to learn it so they can teach it.
In short, we’re doing exactly what we did with the internet.
Many of us had hoped the internet would force education to finally change; that we would at last conduct the long-called-for shift in both the targeted outcomes and the method of engagement of our learning environments. After all, how could we possibly continue the tragic alchemy of turning humans into robotic direction followers and information retrieval systems, when actual robots and search engines were being rolled into classrooms on computer carts?
In the early 2000’s, shortly after the internet proved it was here to stay, Tony Wagner (The Global Achievement Gap, 2008) and Daniel Pink (A Whole New Mind, 2005) called for a rethinking of education in favor of the skills needed to be a thriving individual, partner, team member, worker, and generally employed and fulfilled human in the emerging world. And these calls were backed by a hundred-plus years of similar calls by the likes of Dewey, Duckworth, Vygotsky, Noddings, Freire, Hooks, Illoch, Rogers, Pappert, and more…. all warning of the need to shift our focus in education towards co-exploration, partnership, and real-world engagement in order to enhance our most human skills and capacities, including our ability to learn, and to adapt with disruptive technologies.
Obviously, that shift did not happen.
Tragically, then as now, the approach has been rooted more in saving a threatened and already outdated and woefully insufficient education system, rather than in figuring out how to leverage a disruptive force to create a better system, with better outcomes for individuals, communities, and the world. Once again, we’re slapping a disruptive force into existing structures, tacking it on like 19th century plumbing to the walls of an 18th century home, while riding the inertial trajectory of systems and paradigms that feel too big to transform. There’s tragedy in the fact that our education model was designed to train factory workers, but now the synchronized dances of modern supply chain logistics are already run by rapidly evolving autonomous systems and robots, and yet we’re still educating humans as if the world has not changed.
As a result, we argue that humanity is now even further behind the curve of where we need to be in terms of the outcomes of our education systems, facing accelerating change with insufficient collective capacity to navigate it towards a future of peace and prosperity.
Will this time be different?
We are hoping that as AI cycles up its disruption, it will present education with a clear step-function moment. That is to say, that the dissonance with how the rest of the world is organizing around learning and doing will finally unmask the outdated methods of education to such a blatant degree that schools won’t be able to pretend any more.
Our prediction is that that schools will either redesign learning around co-exploration, partnership, and real-world engagement—as has been shouted from the rooftops for over 100 years—or they will become so irrelevant to the lives students are actually preparing to live that they will give way through sheer market forces to schools (or alternate institutions) that do.
That’s why we are calling for schools to shift from thinking about AI Policy to thinking about AI Strategy, because policy is about compliance, and strategy is about change.
How can we use AI Strategy to rehumanize education?
A viable AI strategy begins not with rules or training sessions, but with shared inquiry.
Step 1: Pilot Co-Learning, Not Compliance
Rather than rolling out mandates, schools invite opt-in teachers (and their students) into structured pilots.
In these pilots:
- Teachers explore how AI can support their own professional practice
- Teachers and students experiment together with AI as a learning partner
- Ethical questions, failures, and tensions are surfaced openly
- Reflection and dialogue are treated as central, not supplemental
This replaces the flawed “learn first, then teach” model with something far more realistic: learning as an ongoing, shared process. This is not only more practical and achievable, but is more representative of the real world of work.
Step 2: Audit the Real World (Together)
And critically, that inquiry must look outward, not just inward. Instead of starting with how AI should be used in school, schools must begin by learning how AI is already being used in the fields students are studying.
Teachers and students, together, explore questions such as:
- How are professionals in this field using AI right now?
- What problems is AI helping solve, and what new risks is it introducing?
- What skills, judgment, and discernment are required to work well alongside AI in this domain?
This reframes AI from an academic-integrity problem into a professional literacy challenge. It grounds learning in reality, not speculation.
Step 3: Learn Before Scaling
The purpose of a pilot is not proof-of-concept. It is pattern recognition.
Schools use interviews, reflection, and documentation to identify:
- Practices that genuinely enhance learning
- Where curriculum and assessment structures break down
- What new skills teachers and students actually need
Only then does it make sense to talk about broader guidance or policy.
Step 4: Build a Living Strategy
Because AI will continue to evolve, strategy must remain adaptive. Schools need processes, not static documents, that allow them to revisit assumptions, update practices, and continue learning together over time, with students as strategic partners.
The Human Outcome We Keep Missing
What often goes unspoken in AI discussions is this: the approach a school takes to AI matters more than the tools themselves.
When schools adopt co-learning, real-world–anchored AI strategies, they are not just building technical fluency. They are developing the human capabilities required to thrive in complexity: judgment, discernment, collaboration, ethical reasoning, and relational intelligence.
These skills cannot be taught through policy documents or curriculum units. They are built through practice: through navigating uncertainty together, making sense of real problems, and learning in relationship with others.
Ironically, AI creates the conditions that finally make this unavoidable. When answers are abundant and automation is cheap, what matters most is how humans work together to decide what to do next.
The Irony: AI as a Path to Rehumanization
Here is the paradox: AI may finally force education to become more human.
For decades, schools have claimed to value collaboration, agency, and lifelong learning while operating on command-and-control models that undermine these very skills. AI makes that contradiction impossible to sustain. When adults can no longer even pretend to be the experts (because no one can possibly keep ahead enough to learn and then teach what is constantly changing), they can be invited back into learning as partners with others, as opposed to deliverers to others.
Used strategically, AI does not diminish the role of educators. It elevates it. Teachers become designers of learning environments, ethical guides, and co-explorers alongside students.
Schools do not need better AI rules.
They need better learning relationships.
And they need them now.
Please reach out. We love to speak with school leaders about their AI strategies. A no-cost, no-expectations initial call could be all the help you need.