You're probably already using AI. Most educators are. So the real question was never whether AI belongs in your work. It's how it shows up, and what it's actually for. Here's how we think about that at Headrush: what we've built, why we built it, and the lines we won't cross.
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Where we stand
We're cautiously optimistic about AI in education, and we mean both of those words.
The promise is real. Used well, AI can hand you back hours, surface the gaps you never had time to go looking for, and lend a hand in the messy middle of a great project, where the work tends to pile up. Ignoring that would be a mistake.
So is pretending there's no risk. Learning happens through struggle. That moment of sitting with a hard idea, turning it over, wrestling it into place, is not a flaw in the process. It is the process. When a learner hands that struggle to an AI, the finished product might show up. The growth usually doesn't.
And learning is deeply human. The hundred small moments of attention you pay a student, like noticing when someone's stuck, asking the right question at the right time, knowing when to push and when to give it room, have no software substitute. That's the heart of teaching.
So we want AI to strengthen what you do, not stand in for it. The line we draw is a simple one. AI should take the lower-value work off your plate, like clicking through menus or translating an idea you already have into a setup, so you have more room for the work only you can do: mentoring, giving feedback, meeting your learners where they are.
Here's the inversion that makes AI genuinely useful: you do the thinking; AI does the building. When you come in with a clear sense of what an experience should do for your students, and you bring your learners, your framework, your school's context, you get something far better than if you'd asked AI to invent it from nothing. What we’re building sits on that idea.
Why we built this
Headrush exists to make great, learner-centered, experience-based learning practical, day to day.
The hardest part of designing these experiences was never the headline moments, like the launch or the final reflection. It's the middle: Scaffolding the journey. Aligning it to your learning targets. Drafting tasks that genuinely invite student voice. Finding the evidence that a learner has actually demonstrated a skill. The middle is where the clock runs out.
Used well, AI gives that time back. Used carelessly, it shortcuts the very thinking that makes deep learning worth doing in the first place.
What it's good at today
Connect Claude or ChatGPT to Headrush, and your assistant can:
Scaffold a learning experience in a few minutes. Generate columns, tasks, and descriptions from a driving question and the methodology you hand it.
Align targets after the fact. Read your school's target sets and suggest alignments for a module or task you've already built.
Provide feedback. Already have something built? Get some feedback and suggestions for improvement. Then have the AI implement those improvements.
Draft and revise. Write a task description in student-friendly language, sharpen a fuzzy learning goal, or drop a peer-critique step exactly where it belongs.
Look things up for you. Answer "what targets are on my current module?" without the tab-switch.
Work inside your framework. Paste in your own design philosophy (Gold Standard PBL, Understanding by Design, your school's homegrown rubric) and the AI will design within it.
The thread running through all of it: you bring the vision and the judgment, and AI handles the work of getting it into the platform.
What it isn't
It isn't autonomous. There's no "AI runs the project for you" mode in Headrush, and we won't be building one.
It isn't an assessor. AI in Headrush won't grade student evidence, decide whether a learner has mastered a target, or make any high-stakes call about a student. Those judgments belong to the educator who knows that learner.
It isn't a content firehose aimed at students. Today, the integration is focused on building learning experiences. It shouldn’t be used to create walls of AI slop for students to wade through.
It isn't a replacement for you. Good teaching and learning is a hundred small acts of attention from someone who's paying attention. AI can clear the runway for those moments. It can't be the one paying attention.
Privacy and your data
When you use AI in Headrush, the assistant only reaches data your account already has permission to see. Anything sent to a model provider is sent for one purpose, answering your request, in inference-only mode, and we don't let our providers train on Headrush data. For the formal language, see our Privacy Policy and Data Processing Addendum.
And if your school has its own guidelines for AI use (we hope it does), those apply here too. We'd rather lean into your school's policy than talk over it.
Where this is headed
A few things we're working on, roughly in order:
AI insights. “Who’s excelling?” “What are many struggling with?” “Based on my student progress, what should my next activity focus on?” The ability to quickly get to the insights you need for your context.
Evidence-aware target alignment. Right now AI can propose alignments. Next, we want it to ground those proposals in the actual student evidence already living in Headrush.
Multi-step workflows with checkpoints. Think "create five versions of this module for my five advisory groups," with a clear preview, a way to edit the differences, and a single confirmation before anything happens.
A thoughtful path to student-facing AI. Carefully. Opt-in. With real teacher controls, and the same posture as everything above: the learner in the loop, you in the loop, never the loop replacing either of you.
See how it works
Read Connecting your AI assistant to Headrush for the practical setup. And if you have questions, whether about how we think, how we handle security, or something we haven't considered yet, reach out via the chat. We'd much rather hear from you early than guess.