Woolis Institutional Resilience Scale 1.0

Education in the Age of AI · A Rapid Governance & Dependency Assessment · 26 Indicators · 5 Standards · 56 Points

Why This Assessment Is Different

Most frameworks for AI in education ask whether institutions are using AI responsibly — what EdTech Hub’s AI Observatory calls Horizon 1: optimizing adoption within existing systems. The Woolis Institutional Resilience Scale 1.0 works at Horizon 2 — the disruption space where adoption is already reshaping what institutions know how to do, who owns their data, and whether they could keep functioning if a critical vendor disappeared tomorrow. It doesn’t ask whether your institution has the right policies. It asks whether the capacity, data rights, infrastructure, pedagogical judgment, and ownership needed to remain an autonomous educational actor are actually in place — or whether they have quietly migrated to someone else.

This assessment is less about policy compliance than institutional stress-testing. It asks what happens when AI systems fail, vendors change terms, staff capacity erodes, or educational judgment quietly migrates to platforms. Each indicator can be answered in under a minute — Strong / In Progress / Absent — based on what your institution actually knows right now. No documentation required.

This is a self-assessment instrument. It is not an audit, compliance review, or external evaluation.

🔒 Your data is secure. Responses are submitted through Jotform, which uses 256-bit SSL encryption and meets GDPR compliance standards. Your name, institution, and assessment responses are used solely by Learning Agenda for research and reporting purposes — including aggregate analysis by role and sector. No student data or personal financial information is collected through this assessment.

Full Name *

Institution *

Your Role *

Your Sector *

What AI governance standards or frameworks, if any, is your institution currently using?

Foundational — most critical — start here

Strategic — deepens resilience once foundational areas are in progress

Supporting — reinforces governance

Standard	Indicator	Response
Capacity & ExpertiseDoes AI adoption build your institution’s capabilities — or hand them to a vendor?	1.1Your institution has mapped which AI tools each unit depends on — and anyone can find the map or an inventory. StrongYou can identify the specific tools and functions right now and could show a list. In ProgressYou have a rough sense but haven't mapped it across all units. AbsentThis hasn't been examined.
	1.2Your institution knows what it would do — step by step — if your primary AI vendor became unavailable for 30 days. StrongA plan exists — even informal — and someone owns it. In ProgressYou've talked about it but nothing is clear enough to act on. AbsentNo plan exists.
	1.3Staff are building skills that would still work if a vendor platform disappeared — not just learning to use the tool. StrongAt least one learning activity per year focuses on transferable skills, not a specific tool. In ProgressIt's happened informally but isn't a regular practice. AbsentAll PD is tool-specific.
	1.4Before adopting a new AI tool, your institution asks what happens to its capacity if that vendor goes away. StrongVendor dependency is part of your adoption conversation before a decision is made. In ProgressYou're aware of it but it hasn't shaped decisions yet. AbsentAdoption decisions don't address this.
	Capacity & Expertise — so far		— / 10
Data Reciprocity & Commercial GovernanceWhen your work generates commercial value for AI vendors, is that relationship on your terms?	2.1Your institution knows — from actually reading the contracts, not assuming — what each AI vendor can do with its data. StrongYou can describe what each vendor can do with your data based on actually reading it. In ProgressYou know for some vendors but not all, or you're relying on what you were told. AbsentNo one has checked.
	2.2Someone has read the actual data-use clauses in your vendor contracts within the past two years and can tell you what they say. StrongSomeone can tell you right now what the data-use clauses say. In ProgressIt's been reviewed for some contracts but not all, or not recently. AbsentContracts haven't been examined for data use.
	2.3Your institution knows whether its vendors offer opt-outs from data training — and has made a deliberate choice, not accepted a default. StrongYour institution has an explicit position on opt-outs — not a default. In ProgressYou know for some vendors but haven't resolved all of them. AbsentThis hasn't been examined.
	2.4Someone responsible has looked closely at what your AI vendors can actually do with your data — not just reviewed the general contract terms. StrongSomeone with responsibility has gone beyond the general terms and examined data rights specifically. In ProgressA review is underway or happened once informally. AbsentNo one has done this.
	2.5When contracts renew, someone checks whether the data terms have changed since last time. StrongThis happened at your last renewal — someone checked and could tell you what changed. In ProgressNo partial credit for Supporting indicators. AbsentRenewals don't include this check.
	2.6Faculty and staff know when their work may be feeding a vendor's AI model. StrongFaculty and staff have been told — in a meeting, policy, or communication — at least once. In ProgressNo partial credit for Supporting indicators. AbsentNo one has been informed.
	Data Reciprocity & Commercial Governance — so far		— / 12
Infrastructure ResilienceIf a major AI platform became unavailable tomorrow, could you keep running?	3.1Your institution knows what it would do and what it would use instead if a major AI vendor ended its contract tomorrow. StrongYou can walk someone through both the process and the alternatives right now. In ProgressYou have a rough sense but haven't mapped it clearly or tested it. AbsentYou don't have a plan or alternatives.
	3.2Your institution knows what student data its vendors hold and how it would get it back. StrongYou can answer both questions for your main vendors right now. In ProgressYou know for some vendors but not all. AbsentYou don't know what vendors hold or how to get it back.
	3.3Leadership regularly asks which AI platforms the institution could not function without. StrongThis has happened in the past 12 months and you can point to when. In ProgressIt's been discussed but not as a deliberate annual review. AbsentLeadership hasn't reviewed vendor dependencies.
	3.4Before adopting a new AI platform, you ask how hard it would be to leave. StrongThis is part of your current adoption process. In ProgressYou've done it once or informally but it's not standard practice. AbsentPortability isn't part of how you evaluate new platforms.
	3.5Your institution has actually tested whether it could exit at least one critical vendor — not just planned it. StrongYou've run an exercise, done a partial migration, or done a contractual audit in the past two years. In ProgressNo partial credit for Supporting indicators. AbsentExit readiness has never been tested.
	Infrastructure Resilience — so far		— / 10
Pedagogical Integrity & Human JudgmentDoes AI adoption strengthen learning — or quietly replace the educator judgment that makes it work?	4.1Your institution has decided which educational decisions — grading, progression, advising — must stay with humans, regardless of what AI can do. StrongYou can state those boundaries right now — they exist as a shared position or clear policy. In ProgressIt's been discussed but no clear position has been established. AbsentNo boundaries have been set.
	4.2Faculty are part of the decisions about AI tools that affect how they teach — not informed afterward. StrongFaculty are part of adoption decisions — not just notified after. In ProgressFaculty are consulted informally or after decisions are made. AbsentFaculty have no role in AI adoption decisions.
	4.3Students are told when AI is shaping their learning or assessment experience — and why. StrongStudents are told — in the course, communications, or institutional policy — when AI plays a significant role. In ProgressSome instructors do this but it's not consistent. AbsentStudents aren't told.
	4.4When your institution adopts an AI tool for learning, it asks whether the tool strengthens educator judgment or quietly substitutes for it. StrongThis question is part of how you evaluate tools before adopting them. In ProgressYou've raised it informally but it's not a standard evaluation criterion. AbsentTool evaluations focus on features and cost, not pedagogical impact.
	4.5Your institution weighs AI adoption decisions against learning outcomes — not just cost and efficiency. StrongAt least one recent adoption decision included a learning outcomes conversation. In ProgressNo partial credit for Supporting indicators. AbsentAdoption decisions are driven by cost and efficiency only.
	Pedagogical Integrity & Human Judgment — so far		— / 11
Stewardship & AccountabilityIs someone specifically accountable for how student data moves through your AI systems — before something goes wrong, not just after?	5.1Someone specific owns how student data moves through your AI systems — and you can name them right now. StrongYou can name that person right now. In ProgressA role exists but it's informal or unclear. AbsentNo one is accountable.
	5.2Any faculty member or student could find out who that person is without having to ask formally. StrongThe answer is findable in under two minutes without asking anyone. In ProgressIt's known internally but not easy to find. AbsentStudents and faculty can't find out without a formal request.
	5.3That person's reach covers your institution's AI systems — not just the data policies from before AI arrived. StrongTheir responsibility explicitly covers AI tools and automated systems — not just older data compliance. In ProgressIt probably covers AI but it's not clearly established. AbsentTheir scope covers legacy compliance only.
	5.4Your institution has recently stopped to ask whether its AI approach still matches reality. StrongYour institution has revisited its AI approach in the past year — you can say when and what changed. In ProgressIt's been discussed but nothing has been deliberately reviewed or updated. AbsentIt hasn't been revisited since initial adoption.
	5.5Before a significant new AI tool goes live, someone with real authority has looked at it and said yes. StrongAt least one recent adoption was reviewed before it went live — you can point to it. In ProgressReview happens sometimes but not consistently. AbsentAdoptions happen without governance review.
	5.6The people responsible for AI aren't just in IT — academic, legal, and procurement leadership are in the room. StrongLegal, procurement, and academic leadership are all involved when AI governance decisions are made. In ProgressNo partial credit for Supporting indicators. AbsentAI governance is an IT function.
	Stewardship & Accountability — so far		— / 13
Total Score		— / 56

Capacity & Expertise

Does AI adoption build your institution’s capabilities — or hand them to a vendor?

1.1Your institution has mapped which AI tools each unit depends on — and anyone can find the map or an inventory.

Response

1.2Your institution knows what it would do — step by step — if your primary AI vendor became unavailable for 30 days.

Response

1.3Staff are building skills that would still work if a vendor platform disappeared — not just learning to use the tool.

Response

1.4Before adopting a new AI tool, your institution asks what happens to its capacity if that vendor goes away.

Response

Capacity & Expertise — — / 10

Data Reciprocity & Commercial Governance

When your work generates commercial value for AI vendors, is that relationship on your terms?

2.1Your institution knows — from actually reading the contracts, not assuming — what each AI vendor can do with its data.

Response

2.2Someone has read the actual data-use clauses in your vendor contracts within the past two years and can tell you what they say.

Response

2.3Your institution knows whether its vendors offer opt-outs from data training — and has made a deliberate choice, not accepted a default.

Response

2.4Someone responsible has looked closely at what your AI vendors can actually do with your data — not just reviewed the general contract terms.

Response

2.5When contracts renew, someone checks whether the data terms have changed since last time.

Response

2.6Faculty and staff know when their work may be feeding a vendor's AI model.

Response

Data Reciprocity & Commercial Governance — — / 12

Infrastructure Resilience

If a major AI platform became unavailable tomorrow, could you keep running?

3.1Your institution knows what it would do and what it would use instead if a major AI vendor ended its contract tomorrow.

Response

3.2Your institution knows what student data its vendors hold and how it would get it back.

Response

3.3Leadership regularly asks which AI platforms the institution could not function without.

Response

3.4Before adopting a new AI platform, you ask how hard it would be to leave.

Response

3.5Your institution has actually tested whether it could exit at least one critical vendor — not just planned it.

Response

Infrastructure Resilience — — / 10

Pedagogical Integrity & Human Judgment

Does AI adoption strengthen learning — or quietly replace the educator judgment that makes it work?

4.1Your institution has decided which educational decisions — grading, progression, advising — must stay with humans, regardless of what AI can do.

Response

4.2Faculty are part of the decisions about AI tools that affect how they teach — not informed afterward.

Response

4.3Students are told when AI is shaping their learning or assessment experience — and why.

Response

4.4When your institution adopts an AI tool for learning, it asks whether the tool strengthens educator judgment or quietly substitutes for it.

Response

4.5Your institution weighs AI adoption decisions against learning outcomes — not just cost and efficiency.

Response

Pedagogical Integrity & Human Judgment — — / 11

Stewardship & Accountability

Is someone specifically accountable for how student data moves through your AI systems — before something goes wrong, not just after?

5.1Someone specific owns how student data moves through your AI systems — and you can name them right now.

Response

5.2Any faculty member or student could find out who that person is without having to ask formally.

Response

5.3That person's reach covers your institution's AI systems — not just the data policies from before AI arrived.

Response

5.4Your institution has recently stopped to ask whether its AI approach still matches reality.

Response

5.5Before a significant new AI tool goes live, someone with real authority has looked at it and said yes.

Response

5.6The people responsible for AI aren't just in IT — academic, legal, and procurement leadership are in the room.

Response

Stewardship & Accountability — — / 13

Total Score — — / 56

Strong = full points | In Progress = partial credit (Foundational & Strategic) | Absent = 0 If evidence is mixed — true for some units or vendors but not all — rate In Progress and note the gap.

Score	Interpretation
0 – 14	Reactive Adoption — AI adoption is occurring with minimal governance coordination or continuity planning.
15 – 28	Emerging Governance — Oversight structures exist but remain uneven, reactive, or dependent on specific individuals.
29 – 42	Structured Governance — The institution has built meaningful oversight, shared accountability, and operational resilience.
43 – 56	Resilient Institutional Stewardship — AI adoption is held in place by durable institutional capacity, shared ownership, and continuity-oriented practice.

Woolis Institutional Resilience Scale 1.0

Why This Assessment Is Different

HomeFrameworkUNBOUNDResourcesAboutContactAbout the Icons

Home
Framework
UNBOUND
Resources
About
Contact
About the Icons