GenAI in academia is a tremendous opportunity — and a significant risk. Wonders is committed to institutionally safe AI that promotes academic integrity, prepares students for AI-driven work, and respects every dimension of institutional need.
The reality
92% of students already use AI. Nearly a third have used it to cheat — and three-quarters of those believe cheating is wrong but do it anyway. Most say their institution has no clear policy. Banning it hasn’t worked; pretending it away hasn’t either.
Sources: HEPI Student GenAI Survey (2025); Programs.com (2025). Students reach for unsanctioned tools when sanctioned ones don’t fit the work.
What we believe
The commitments behind every decision about how Wonders handles AI — and how we’d like to be held to account.
AI should make you sharper, not lazier.
The point of a research tool isn’t a faster answer — it’s a better researcher. If a tool makes thinking optional, it has failed the student.
Humans decide; AI guides.
Nothing meaningful happens without a person in the loop. AI can surface, suggest, and organise — it shouldn’t author the work or the judgement.
Provenance over trust.
Every claim should trace back to a real source. “Trust me” isn’t good enough in scholarship — and it isn’t good enough coming from software either.
A student’s work is the student’s.
We never train models on student or institutional work. Their ideas don’t quietly become someone else’s training data.
Level the playing field — don’t tilt it.
Access should be equitable: the same capability for every student, not an edge for whoever can afford the better chatbot.
Vendors carry responsibility too.
The people who make a tool should build integrity into its core — not profit from undermining it. We hold ourselves to that, and we think you should hold us to it.
“The question was never whether students would use AI. It’s whether it makes them think more, or less — and we built Wonders for more.”
Joe Pacal — Co-founder & CEO, WondersThe framework
So we built a way to tell them apart. Drawing on an analysis of 122 institutional AI policies, our assessment framework runs a tool through the questions that actually matter — before it ever reaches a student. The uncomfortable part: most policies treat every tool as equivalent. They aren’t.
Governance
Ethics, legal & IP, privacy and security, attribution, misconduct, and ROI.
Pedagogy
Learning outcomes, assessment integrity, critical AI literacy, and cheating.
Operations
Evaluation, monitoring, secure deployment, and training & support.
Student needsmost frameworks skip this
Does it actually solve a student’s problem — better than the unsanctioned tool they already use?
Vendor responsibilitymost frameworks skip this
Does the vendor build academic integrity in — or quietly profit from undermining it?
A living draft — built on literature review and expert input, not yet primary research. We re-evaluate it as the field and the tools evolve.
Research-backed references for the people writing AI policy, assessing risk, and bringing GenAI to campus responsibly. Free for educators and institutions — we’ll send the latest versions.
Request the resources →White paper
The State of Educational GenAI
An exploration of GenAI readiness, student perspectives, and institutional policy across higher education.
Reference guide
Institutional AI Policy Reference
A reference of 122+ institutional AI policies from leading universities worldwide, gathered in one place.
Templates
Sample AI Use Policies
Research-based sample AI use policies to adapt as a starting point for your own.
Framework
AI Risk Assessment Framework
A draft framework for evaluating AI tools before piloting or procurement. Last updated November 2025.
We’re actively collaborating with partner universities on safe, responsible AI adoption — and we’d genuinely value your perspective. Tell us how you’re thinking about it.
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