MUSH Towards
the Future
Five landmark reports on AI in education — mapped to the MUSH framework.
MAP · UPSKILL · SPARK · HARNESS
A starting point for school leaders navigating AI policy.
Where Are You Starting From?
Before you read the reports, take a moment. These eight reflection questions — two for each MUSH phase — help you locate where your school is right now. Pick one from each phase and bring it to your next team conversation.
Read the Trail You’re On
You just sat with eight reflection questions. Some came easily. Others didn’t. That tension is information. The places where you paused longest, where the answer felt uncertain or incomplete — those are the stretches of trail that need the most attention. Each MUSH phase below names its two questions and points you toward the research that can help you find footing.
Rate your tension on each MUSH question and get tailored reading prescriptions from 5 landmark reports.
Read the Trail Before You Run It
What future is already taking shape here through everyday AI use, and what evidence from classrooms, student work, staff practice, and conversation tells us that?
Which habits, policies, or stories about teaching and learning have hardened our path towards change?
If these were hard to answer, start here:
→ Brookings: A New Direction for Students in an AI World→ Stanford: Understanding the Evidence Base for AI in K–12
Strengthen the Team Before You Speed Up
If we are truly addressing a context-specific issue, what should we be able to notice in practice three months from now?
Where are the emerging leaders in this school who show curiosity and a willingness to learn in public, and how might we invite them to connect and lead together?
If these were hard to answer, start here:
→ EU: Guidelines for Educators on AI in Education→ Ireland: AI in Schools – A Guide
Testing Tiny Flickers of Innovation
Where and when might we invite our community to share sparks of learning that they may not yet realize are more significant than they realize?
What one pilot can I engage others with? How can I document my learning? What do I want to demonstrate to others through this pilot?
If these were hard to answer, start here:
→ Stanford: Understanding the Evidence Base for AI in K–12→ EU: Guidelines for Educators on AI in Education
Co-Create Scaffolding to Bring It All Together
When exactly are students part of the team reading the trail, people whose experiences and concerns shape our choices, rather than as passengers on the sled?
In this period of transformation, what do we most need to help one another protect, practice, and become?
If these were hard to answer, start here:
→ UNESCO: AI Competency Framework for School Students→ Brookings: A New Direction for Students in an AI World
The MUSH Framework
MUSH is a framework for navigating AI in education — built on the metaphor of the sled dog team. Each letter is a phase of the journey. Each report on this page maps to a phase.
Why the Musher?
A musher stands on the runners — feeling the terrain shift, reading the team, making calls in real time. That’s what leading a school through AI feels like right now.
That’s the leadership these reports are calling for — even if they don’t use the word.
Full Trail Glossary
Every term on this page comes from the world of dog mushing. Here’s the full vocabulary, mapped to navigating AI in education.
Musher
The person who drives and manages the sled and team. That’s you — the educator steering the direction.
Gangline
The main line running from sled to dogs. The shared vision connecting policy to practice.
Tugline
Connects each dog’s harness to the gangline. Individual connections that let each stakeholder pull their weight.
Snowhook
A metal anchor to hold the sled when stopped. Knowing when to stop and reassess before moving on.
Bivouac
A temporary camp in rough conditions. Rapid-response policies built while terrain keeps shifting.
Sastrugi
Hard ridges of wind-shaped snow. Equity gaps, infrastructure limits, and funding barriers that make the trail uneven.
Overflow
Water risen over ice, creating dangerous slush. When adoption outpaces governance and the ground gives way.
Break Trail
Going first through fresh snow. The reports and pioneers who go first so others can follow.
Route Finding
Choosing the safest path through terrain. Evidence-based decision making in uncertain conditions.
Basecamp
Starting point for travel, rest, and supplies. The foundational policies schools build from.
Booties
Protective coverings for a dog’s paws. Data privacy, consent, and safety measures that shield students.
Harness
Gear that lets each dog pull safely. The ethical frameworks that let us use AI without harm.
Trail Questions: Pair the Vocabulary, Find Your Path
Each question below pairs two mushing terms. Use them to spark reflection with your team — in a meeting, a planning session, or a quiet moment on the trail.
- 🧦Booties + Overflow: If student data protections are our booties, what happens when AI adoption overflows faster than we can fit them?
- 🏕Basecamp + Sastrugi: We’ve built a basecamp policy — but how do we move forward when sastrugi like funding gaps and infrastructure limits block the trail?
- 🧭Route Finding + Break Trail: Who’s doing the route finding on AI evidence, and which reports are breaking trail so the rest of us don’t have to start from scratch?
- 🨢Gangline + Musher: What’s the gangline that keeps our team connected — and does the musher know where we’re actually headed?
- ⚓Snowhook + Bivouac: When do we throw the snowhook and pause — and when do we set up a bivouac and build something temporary while the terrain keeps shifting?
- 🔗Harness + Tugline: The ethical framework is our harness — but what’s the tugline connecting each teacher’s daily practice to it?
Where Do You Start?
You don’t have to read all five. Start with the phase that matches where you are right now.
MAP
You need the big picture before you move. Start with Brookings for the global landscape, then UNESCO to see what’s happening on the corporate side.
UPSKILL
You need to know what actually works. Start with Stanford. It tells you where the evidence is firm — and where you’re flying blind.
SPARK
You need a starting point you can act on Monday. Start with Ireland. It’s the most practical, school-level guidance of the five — and it’s built to evolve.
HARNESS
You need the ethical framework to pull hard without causing harm. Start with the EU guidelines. They give your team the structure to move fast and stay safe.
All Five Reports
Five Reports. One Trail Forward.
Ready to go deeper? Each card below unpacks one report — the key question it answers, the mushing concept it maps to, and what makes it worth your time.
A New Direction for Students in an AI World
Brookings • January 2026 MUSH Phase: MAPDo the risks of AI for students currently outweigh the benefits — and what would it take to reverse that?
Lead Dog: The dog at the very front that follows commands and guides direction. This report leads the pack — the most comprehensive global study, surveying 500+ stakeholders in 50+ countries, reviewing 400+ studies, organized into three pillars: Prosper, Prepare, Protect.
Read the Full Report →The Evidence Base on AI in K-12: A 2026 Review
Stanford SCALE • 2026 MUSH Phase: UPSKILLWhat does the causal evidence actually tell us about AI’s impact on students and teachers — and where are the gaps?
Route Finding: The skill of choosing the safest or most efficient path through terrain. Out of 800+ papers, only 20 produced strong causal evidence. Zero high-quality K-12 causal studies in the U.S. This report maps where firm ground exists and where it doesn’t.
Read the Full Report →Guidelines on the Ethical Use of AI and Data in Teaching and Learning
European Commission • Updated 2026 MUSH Phase: HARNESSHow should educators ethically assess, adopt, and govern AI tools in their daily practice?
Harness: The gear worn by each dog so it can pull safely and efficiently. These guidelines are the harness — the ethical framework that lets teachers use AI without causing harm. Grounded in the EU AI Act, with guiding questions and regulatory context.
Read the Full Report →Guidance on Artificial Intelligence in Schools
Ireland Dept. of Education and Youth • October 2025 MUSH Phase: SPARKWhat does a school-level starting point for AI adoption actually look like — from policy to classroom?
Basecamp: A main camp used as a starting point for travel, rest, and supplies. Ireland’s guidance is a national basecamp — a practical, school-level entry point with a 4P approach, designed as a living document that evolves with the terrain.
Read the Full Report →Responsible AI in Practice: 2025 Global Insights
UNESCO & Thomson Reuters Foundation • 2026 MUSH Phase: MAPIs corporate AI adoption outpacing governance — and what does that mean for the systems schools depend on?
Overflow: Water risen over ice, creating dangerous slush. Of 3,000 companies studied, only 1 in 10 committed to an AI governance framework. This report is the overflow warning — products entering schools are built faster than the rules governing them.
Read the Full Report →