A growing dissonance is becoming impossible to ignore: the gap between what most young people experience in schools and what they actually need to thrive in this moment and beyond.
For the past few years, we’ve been obsessed with the question of what ongoing advancements in AI mean for the future of school. We’ve watched as districts chase efficiency through disconnected pilots. We’ve seen AI readiness appear in glossy materials without changing classroom practice.
And we’ve wondered: What if there’s a better way?
Our new report maps out two diverging paths schools can take with AI: the “probable path” where we’re trapped in fragmented innovation and the “preferable path” where AI is a catalyst for the kind of learning young people need.
Probable vs. Preferable: A Fork in the Road
Our research maps out two diverging paths schools can take with AI—and the gap between them has never felt more urgent to address.
The Probable Path
This is where we’re headed if we don’t act intentionally: fragmented innovation that optimizes what’s easily measured, automates tasks without transforming experiences, and leaves students more isolated than empowered. AI becomes a tool for efficiency rather than a catalyst for extraordinary learning.
The Preferable Path
This is what becomes possible when we design with intention: AI that personalizes learning, deepens relationships, and connects school to the world in ways that were difficult—if not impossible—to scale before. Young people develop the skills, mindsets, and agency they need to thrive in an uncertain future.
The choices we make over the next few years—from practitioners to product developers to funders and policymakers—will determine which path we take.
What This Looks Like in Practice
To bring the preferable path to life, we explored what it could look like across three learning contexts in the next 2-3 years:
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K-2 Humanities: Building Stories, Building Community
Marco and his classmates explore Whose stories make up our community? through a blend of play, literacy, and technology. When Marco shares in Spanish, AI translates for his classmates. He interviews his grandfather using AI-generated prompts, creates a multimedia collage combining art and sound, and adds it to his digital portfolio with reflections on how different forms tell stories—all while Mr. Hafeez uses freed-up time to focus on relationships rather than paperwork. -
Middle School Math: Solving Real Problems That Matter
Amara’s team redesigns their school courtyard to reduce heat. She practices ratios on an adaptive app while her peer advances to slope. Outside, they collect temperature data and debate whether to trust AI simulations or their own measurements. Ms. Sun asks them to defend their reasoning without tech. Later, AI helps visualize civic impacts and connects them with a climate scientist. Walking home, Amara surprises her mom by explaining the math behind newly planted trees. -
11th Grade: Connecting School to Career and Purpose
At her housing nonprofit internship, Jade maps where affordable housing is needed based on access to transit and resources. When her data shows neighborhoods that look good on paper but lack bus lines, she and her mentor Laura discuss whether AI weighted variables fairly. In Civics class, Jade recognizes how AI can misrepresent “opportunity”—and her teacher reminds the class that good design starts with empathy. In advisory, Jade connects her data work to values of fairness and belonging, seeing how her skills align with future purpose.
The Path Forward
Whether we continue along the probable path or move to the preferable one depends on the actions of several different ecosystem groups. Lasting change will require cross-sector coalitions—policymakers, state and district leaders, researchers, public and professional will, support organizations, funders, product developers, higher ed institutions and employers—working together across five critical priorities:
- The field must develop shared frameworks, curricula, and assessment tools for AI literacy as a core outcome.
- Assessment and accountability systems must enable flexible assessment windows and a wider range of ways for students and schools to demonstrate progress while holding rigorous standards across a range of holistic outcomes.
- Educator roles must evolve for an AI era, requiring aligned changes to certification pathways, preparation programs, evaluation frameworks, and support systems.
- Infrastructure for AI-enabled learning must be secure, interoperable, ethical, and evidence-based.
- The ecosystem must lay the foundation for safe experimentation, create conditions for flexible innovation, and build capacity for continuous, coherent design.
