Woolis Institutional Resilience Scale 1.0
Education in the Age of AI · A Rapid Governance & Dependency Assessment · 26 Indicators · 5 Standards · 56 Points
© 2025 Diana D. Woolis · Learning Agenda. Free for non-commercial educational use with attribution. Commercial use, reproduction for distribution, or adaptation requires written permission. LearningAgenda.org · dwoolis@LearningAgenda.org
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.
A 2026 global Delphi study by Crompton et al. — drawing on experts across 22 countries — identified eight consensus areas for AI governance in higher education, including academic integrity, privacy, and human oversight. The Woolis Institutional Resilience Scale builds on that foundation but asks the harder question: what would actually bend, hold, or break when those structures meet real conditions.
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.
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. |
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| 1.2Your institution knows what it would do — step by step — if your primary AI vendor became unavailable for 30 days. |
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| 1.3Staff are building skills that would still work if a vendor platform disappeared — not just learning to use the tool. |
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| 1.4Before adopting a new AI tool, your institution asks what happens to its capacity if that vendor goes away. |
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| 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. |
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| 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. |
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| 2.3Your institution knows whether its vendors offer opt-outs from data training — and has made a deliberate choice, not accepted a default. |
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| 2.4Someone responsible has looked closely at what your AI vendors can actually do with your data — not just reviewed the general contract terms. |
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| 2.5When contracts renew, someone checks whether the data terms have changed since last time. |
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| 2.6Faculty and staff know when their work may be feeding a vendor's AI model. |
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| 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. |
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| 3.2Your institution knows what student data its vendors hold and how it would get it back. |
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| 3.3Leadership regularly asks which AI platforms the institution could not function without. |
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| 3.4Before adopting a new AI platform, you ask how hard it would be to leave. |
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| 3.5Your institution has actually tested whether it could exit at least one critical vendor — not just planned it. |
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| 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. |
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| 4.2Faculty are part of the decisions about AI tools that affect how they teach — not informed afterward. |
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| 4.3Students are told when AI is shaping their learning or assessment experience — and why. |
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| 4.4When your institution adopts an AI tool for learning, it asks whether the tool strengthens educator judgment or quietly substitutes for it. |
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| 4.5Your institution weighs AI adoption decisions against learning outcomes — not just cost and efficiency. |
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| 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. |
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| 5.2Any faculty member or student could find out who that person is without having to ask formally. |
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| 5.3That person's reach covers your institution's AI systems — not just the data policies from before AI arrived. |
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| 5.4Your institution has recently stopped to ask whether its AI approach still matches reality. |
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| 5.5Before a significant new AI tool goes live, someone with real authority has looked at it and said yes. |
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| 5.6The people responsible for AI aren't just in IT — academic, legal, and procurement leadership are in the room. |
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| Stewardship & Accountability — so far | — / 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 | High Dependency — Your institution's capacity to function is significantly tied to vendor continuity, individual knowledge holders, or external systems it doesn't control. |
| 15 – 28 | Emerging Awareness — Some dependencies have been identified and some risks are understood, but the institution hasn't yet built the capacity to act on them consistently. |
| 29 – 42 | Building Resilience — The institution understands its dependencies and is actively building the capacity, relationships, and practices needed to operate with greater autonomy. |
| 43 – 56 | Operational Resilience — The institution's capacity, data relationships, infrastructure, and stewardship are genuinely its own — and would hold when conditions change. |
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