The right question is not which AI tool is “best.” It is whether a specific tool is safe, accessible, reliable, affordable, and governable for a defined university task. Start by documenting the task, users, data, expected benefit, and consequences of an error. Then verify data practices in the contract, test the actual workflows with disabled users and assistive technology, measure performance on representative tasks, calculate the full institutional cost, and set a date to reassess the decision.
This process can approve a low-risk pilot while rejecting the same product for a high-stakes student or employment decision. It also gives procurement, technology, privacy, accessibility, legal, and academic or operational leaders a shared record of why the decision was made and what would trigger a pause or exit.
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Use a decision process, not a product ranking
An AI product can be useful for brainstorming yet unsuitable for advising a student, grading work, allocating financial aid, or making an employment decision. Suitability depends on the task, the information supplied to the system, who is affected, and what happens when the output is wrong. A university evaluation should therefore produce a documented decision for one use case, not a universal rating of a vendor.
Use five gates in sequence:
- Purpose and risk: define the task, users, data, alternatives, and consequences of error.
- Privacy and security: establish what is collected, retained, shared, trained on, and deleted.
- Accessibility: test the workflows people must use, with the assistive technologies they rely on.
- Accuracy and accountability: test representative work, document failure modes, and assign human review.
- Cost and flexibility: compare total cost, implementation effort, support, and the ability to change or leave.
Do not allow a polished demonstration, a completed questionnaire, or a compliance label to substitute for these gates. Each is evidence to examine, not proof that the tool fits every university context.
1. Define the proposed use before reviewing a tool
Write a one-page use-case statement before inviting vendors to demonstrate anything. This prevents a generic demo from driving a decision that the institution has not actually made.
Record the task and expected benefit
- State the exact task, such as drafting routine email, summarizing research supplied by a faculty member, answering library questions, or extracting fields from a form.
- Name the users and affected people: students, faculty, staff, applicants, research participants, patients, or members of the public.
- Describe the measurable benefit you expect, such as shorter response time, improved access to information, or reduced manual transcription.
- List non-AI alternatives, including an existing licensed feature, a conventional workflow, a limited pilot, or a locally managed service.
- Identify the institutional policy, records schedule, accessibility requirement, research protocol, or legal review that applies.
Classify consequences of an incorrect output
| Risk level | Typical use | Minimum controls |
|---|---|---|
| Lower | Optional brainstorming or formatting where a person independently checks the result | No sensitive data; clear user notice; easy correction; human review before anything is sent or published |
| Moderate | Internal summaries, tutoring suggestions, or workflow drafts that influence a later decision | Approved data types; documented verification; logging; defined escalation when the system is uncertain or wrong |
| Higher | Admissions, grading, discipline, accommodations, employment, financial aid, health, safety, or legally significant communications | Formal risk and legal review; strict data controls; representative validation; named decision owner; human approval; monitoring and an immediate stop procedure |
The classification is an institutional judgment. A tool does not become low risk merely because a vendor markets it as an assistant. If the proposed use affects rights, eligibility, access to services, grades, employment, or safety, treat it as high consequence until the responsible offices document otherwise.
2. Examine privacy, security, and data-use terms
Ask the vendor to answer in plain language and attach the answers to the purchase record. Marketing language such as “secure” or “private” is too vague to evaluate.
Build a data map
List every data element that could enter the service:
- Prompts, uploaded files, generated outputs, account identifiers, IP addresses, device information, and usage telemetry.
- Student records, employee information, research data, health information, confidential legal material, unpublished work, or other regulated or contractually restricted content.
- Data sent to plug-ins, analytics providers, hosting companies, model providers, or other subprocessors.
For each element, record the purpose, system location, retention period, access roles, export method, and deletion trigger. Include data copied into logs, support tickets, backups, evaluation datasets, and model-training pipelines.
Questions the contract should answer
- Are prompts, files, and outputs retained? If so, for how long, in which systems, and under what deletion schedule?
- Are institutional or student inputs and outputs used to train or improve a shared model, an institution-specific model, or neither?
- Can the vendor, its staff, or subprocessors view content? Under what support, security, or legal circumstances?
- In which countries and data centers are content and metadata processed or stored?
- Can the university disable model training, telemetry, plug-ins, or other data sharing independently for its tenant?
- Can administrators export prompts, outputs, audit records, and configuration data in a usable format?
- What happens to data, backups, and derived model artifacts when the contract ends, a tenant is closed, or the vendor changes its service?
- How are security incidents, subpoenas, compelled disclosures, and subprocessor changes reported?
- Does the vendor reserve generated results to the institution, or could similar content be exposed to other customers or users?
EDUCAUSE identifies security, compliance, institutional-data use for model training, and transparency about newly added AI features as procurement concerns. A product may add an AI capability to an existing service without a prominent notice, so require change notifications and a right to reassess or disable the feature.
Do not treat a compliance claim as a decision
Do not conclude that a product is “FERPA compliant” from a marketing statement or checklist alone. The university must assess the particular data, purpose, users, contract, retention, and applicable law with its privacy, records, security, and legal offices. The same service can be acceptable for public information and prohibited for identifiable student records.
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Contractual promises matter only if administrators can operate them. During a pilot, verify that an administrator can change retention settings, disable a feature, remove a user, retrieve records, and confirm deletion. Record the time and evidence for each action. If a control exists only through a support request, document the service-level commitment and escalation path.
3. Test accessibility in the real workflow
Accessibility documentation is a starting point, not a substitute for use by people with disabilities. Include the campus accessibility office and affected users before purchase, not after a barrier becomes an accommodation case.
Review documentation and its limits
Request the current accessibility conformance report or equivalent documentation, known limitations, remediation backlog, testing methods, and the date of the last update. Compare the claims with the workflow your university intends to deploy. The W3C Web Accessibility Initiative’s WCAG 2 Overview is a standards reference; it does not establish that a particular product conforms.
An anonymous respondent in EDUCAUSE’s May 2025 QuickPoll described “lack of accessibility compliance with new AI tools” as a procurement concern. Treat that concern as a reason to test, not as evidence that every new tool fails.
Run a task-based accessibility test
- Complete the full workflow with keyboard-only navigation, including sign-in, file upload, editing, submission, error recovery, and export.
- Use the screen readers and browsers supported on campus. Check headings, labels, focus order, live updates, status messages, dialogs, and error announcements.
- Test zoom, reflow, contrast, text spacing, pointer alternatives, and operation without time limits where the task requires them.
- Check captions, transcripts, audio descriptions, and alternatives for generated or embedded media.
- Test pasted content, tables, mathematical notation, code, scanned documents, and downloaded files for accessible structure.
- Ask users with disabilities to perform representative tasks without coaching. Record completion, workarounds, extra effort, and points where assistance was required.
- Verify that administrators can remediate a barrier or provide an equivalent alternative without exposing private information.
Document each issue with the workflow, assistive technology, browser, date, severity, workaround, owner, and vendor response. A completed vendor questionnaire can inform this record but cannot replace it.
Learn from institutional examples without overgeneralizing
A February 2024 EDUCAUSE case study describes the University of Michigan’s 2023 effort to build closed generative-AI tools with privacy, accessibility, affordability, and equitable access in mind. Vice president for information technology and chief information officer Ravi Pendse said, “Our vision at the University of Michigan is to be a leader when it comes to generative AI,” while ITS executive director Robert Jones said, “There are a lot of people playing in this space, but we didn’t see anyone marching forward with inclusion, equitability, and accessibility at the forefront.” The case illustrates why design and testing choices matter; it is not evidence that current commercial products meet a different university’s requirements.
4. Evaluate accuracy, reliability, and bias for the intended task
There is no meaningful single accuracy score for an AI tool. Performance depends on the prompt, source material, language, subject, user skill, model version, and whether the task requires retrieval, calculation, classification, or generation. Vendor demonstrations show possibility, not institution-wide performance.
Design a representative test set
Use de-identified or synthetic material that reflects the real workflow. Include normal cases, incomplete or ambiguous requests, unusual terminology, multiple languages where relevant, and cases where the correct behavior is to ask a person or abstain.
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|---|---|---|
| Factual correctness | Errors, unsupported claims, wrong calculations, and citations that do not support the answer | Can a trained reviewer detect and correct errors before harm occurs? |
| Completeness | Omitted requirements, missing exceptions, and lost context in summaries | Does omission create an unacceptable risk for this task? |
| Consistency | Variation across repeated runs, model versions, prompts, and equivalent inputs | Can users rely on a stable process and documented version? |
| Equity | Uneven quality, refusal, or tone across relevant names, languages, dialects, disability-related content, or demographic contexts | Are differences understood, mitigated, and acceptable to the use-case owner? |
| Explainability and recovery | Source visibility, uncertainty signals, correction tools, and escalation behavior | Can a person understand, challenge, and safely override the output? |
Have subject-matter experts establish the expected answer or acceptable range before viewing system outputs. Keep the prompt, source version, model or feature version, output, reviewer decision, and reason for failure. The record lets the university repeat the test after a model or product update.
Set human-review rules
- Name the person accountable for the final decision; “the user” is not an adequate control when users lack subject expertise.
- Define which outputs require line-by-line verification, a second reviewer, or mandatory escalation.
- Prohibit automatic action when the system expresses uncertainty, lacks source support, or handles a high-consequence case.
- Give users a way to report an error and preserve the relevant input and output for investigation without retaining unnecessary personal data.
The available higher-education sources identify output quality, accuracy, equity, and risk as concerns but do not establish an independent error rate or head-to-head ranking for current vendors. Do not describe a tool as accurate in the abstract.
5. Calculate the full institutional cost
Compare the cost of the complete service, not just the subscription line. Build a model for the pilot and for the likely scale if the pilot succeeds.
| Cost category | Questions to include |
|---|---|
| Licensing and usage | Per-user, seat, token, transaction, storage, premium-model, or overage charges; minimum commitments; price changes |
| Implementation | Single sign-on, identity integration, data migration, configuration, accessibility remediation, and testing |
| Operations | Administration, monitoring, prompt or content management, incident response, records handling, and model-change review |
| People and support | Training, help-desk workload, instructional design, subject-matter review, privacy counsel, and procurement effort |
| Expansion | Marginal cost of adding departments, peak demand, additional languages, storage, or accessibility alternatives |
| Exit | Data export, replacement system, contract termination, migration, and costs of retaining a safe alternative |
Define the benefit in the same units as the cost model: staff hours avoided, response time, service capacity, or a documented quality improvement. Include the cost of human verification; automation that shifts work to review staff is not free.
Put survey figures in context
EDUCAUSE and the American Council on Education reported in March 2025 that 19% of surveyed institutions were budgeting for anticipated long-term AI costs, 34% of executive leaders thought their institution had probably underestimated AI-related costs, and 45% reported partnering with external sources to share AI investment costs. These are survey results, not estimates for an individual university.
EDUCAUSE’s QuickPoll, fielded May 12–14, 2025, with 270 member responses to 16 questions, found that 32% selected costs or lack of budget as a leading procurement challenge and 21% selected opaque contract terms. The poll was distributed to EDUCAUSE members, cannot be associated with specific institutions, and is not a census of universities.
Rank #4
- Presents guiding principles and action steps that address both the issues and the opportunities that come with artificial intelligence
- Learn how to cultivate a schoolwide understanding of AI,
- Implement student-centered practices that support academic integrity
- Ensure that effective teaching and learning remain the school’s top priority
Prefer flexible commitments while learning
In a fast-changing market, a short pilot, renewal checkpoint, usage cap, or department-limited rollout can protect the institution while it gathers evidence. Negotiate price protection, transparent usage reporting, advance notice of material feature or model changes, and a practical termination and export process. A low introductory price can conceal integration, support, accessibility, and expansion costs.
6. Compare deployment approaches
Include more than one feasible path in the business case. The choice is not simply “buy or do nothing.”
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|---|---|---|
| Buy a vendor product | Faster deployment, mature support, existing integrations, and predictable feature roadmap | Data-control limits, changing terms, opaque model behavior, recurring fees, and dependence on one supplier |
| Limit the purchase to a pilot | Small exposure, measurable learning, and a defined renewal decision | Pilot results may not represent campus scale; temporary workarounds and duplicate systems can add cost |
| Use an already licensed institutional suite | Existing identity, contracting, support, and governance relationships may reduce implementation effort | AI features may be added to the suite without clear notice; verify separate retention, training, and accessibility terms |
| Develop or host a controlled institutional service | Greater control over data boundaries, configuration, accessibility design, and change management | Engineering, security, model operations, support, evaluation, and ongoing infrastructure costs remain with the university |
The University of Michigan case describes a closed environment and free access to U-M GPT for its community at launch, with departmental charging for its Maizey platform. Those were details of that institution’s 2023 experience, not a universal cost or deployment recommendation.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.7. Run a cross-functional procurement review
Assign one accountable owner, but involve every group that owns a material risk. In EDUCAUSE’s 2025 QuickPoll, 94% of respondents reported technology involvement in AI procurement, 85% reported cybersecurity or data-privacy involvement, and 58% reported teaching-and-learning involvement. A university should also include procurement, accessibility, legal counsel, records management, finance, and the academic or operational team that will use the system.
Use HECVAT 4 as an evidence-gathering tool
HECVAT 4, the higher-education vendor assessment toolkit, adds AI-specific questions drawing on multiple AI and cybersecurity frameworks. Use it to request consistent answers, identify missing evidence, and route follow-up questions to the appropriate risk owner. A completed HECVAT is not certification that a product is safe, accessible, accurate, or suitable for every context.
An EDUCAUSE webinar dated August 31, 2026, describes possible uses of AI to summarize HECVAT responses and identify follow-up questions. Automated assistance can reduce clerical work, but professional judgment and human expertise remain central; do not let an automated summary approve a vendor.
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Document the decision in ten steps
- Approve the use-case statement and risk classification.
- Inventory data and prohibit unapproved categories during evaluation.
- Send privacy, security, accessibility, integration, pricing, and exit questions to the vendor.
- Review the answers with the responsible institutional offices.
- Run accessibility tests with affected users and assistive technologies.
- Run the representative accuracy, bias, and recovery test set.
- Build pilot and scale cost models, including staff time and exit costs.
- Negotiate controls, change notices, audit rights, deletion, export, support, and termination terms.
- Record approval conditions, human-review duties, incident contacts, and prohibited uses.
- Set a review date and an owner for monitoring after launch.
8. Turn findings into a decision scorecard
A scorecard makes trade-offs visible, but it should not hide a blocking failure behind a high average. Set minimum thresholds for privacy, accessibility, and high-consequence accuracy before adding any weighted scores.
| Gate | Evidence required | Blocker examples |
|---|---|---|
| Purpose and risk | Approved use case, alternatives, affected users, and consequence analysis | No accountable owner or no lawful, policy-approved purpose |
| Privacy and security | Data map, contract answers, retention and deletion evidence, incident process | Unacceptable training use, uncontrolled subprocessor access, or no workable exit |
| Accessibility | Current documentation and task-based user testing | People cannot complete a required workflow and no equivalent path exists |
| Accuracy and equity | Representative test results, reviewer rules, version record, and remediation plan | Uncorrectable errors or disparities in a consequential task |
| Cost and flexibility | Pilot and scale budgets, support model, price-change and termination terms | Unbounded usage exposure or a cost that exceeds the documented benefit |
| Governance | Named owners, training, logging, incident response, and review date | No way to monitor changes or suspend the service |
Record “not established” when evidence is missing. A blank cell should trigger a follow-up question, not an optimistic assumption.
9. Plan for changes after purchase
Approval is the beginning of oversight. Models, interfaces, subprocessors, prices, accessibility behavior, and terms can change without matching the version tested during procurement.
- Review release notes and contract notices for new AI features, model substitutions, data-use changes, and subprocessor changes.
- Repeat a small regression test after material updates, including accessibility tasks and known failure cases.
- Monitor incidents, complaints, accessibility barriers, cost per task, usage growth, and human-review workload.
- Recheck whether the original benefit is being achieved and whether users have adopted unsafe workarounds.
- Keep a suspension procedure that preserves records and provides a safe alternative workflow.
- Reassess at renewal and whenever the use case, data, law, or consequence of error changes.
Only 36% of respondents with AI procurement processes in the 2025 EDUCAUSE QuickPoll reported reviewing products after initial procurement against AI-related questions. Treat post-purchase review as a required control, not an optional maturity goal.
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Starting with a favorite product
Failure: stakeholders select a tool from a demo and retrofit a use case. Fix: approve the task, data, risk, and alternatives before demonstrations.
Accepting a policy link as a privacy review
Failure: a general privacy policy is treated as an answer about institutional prompts and retention. Fix: obtain tenant-specific settings, contractual terms, subprocessors, deletion evidence, and change-notice commitments.
Testing only the easiest prompts
Failure: a few polished examples are used to claim accuracy. Fix: test normal, ambiguous, adversarial, multilingual, and abstention cases with an agreed answer key.
Delegating accessibility to a questionnaire
Failure: a vendor statement is accepted without user testing. Fix: observe real tasks with keyboard navigation, screen readers, captions, zoom, and representative documents.
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Budgeting only the license
Failure: the business case omits integration, training, review, support, and exit. Fix: model pilot, scale, and termination costs separately.
Stopping oversight at contract signature
Failure: the institution never checks a changed model or newly added feature. Fix: assign an owner, require notices, run regression tests, and schedule renewal review.
A concise approval checklist
- The use case, users, data, benefit, alternatives, and error consequences are written down.
- Restricted data categories and prohibited uses are explicit.
- Retention, training use, subprocessors, processing locations, deletion, export, and incident terms are documented.
- Accessibility testing includes affected users and the assistive technologies used on campus.
- Accuracy and equity tests use representative tasks and define when a person must approve the result.
- Licensing, usage, implementation, support, training, accessibility remediation, expansion, and exit costs are in the model.
- HECVAT 4 or an equivalent assessment has been reviewed by the appropriate risk owners.
- The contract includes change notices, practical controls, and a suspension or termination path.
- A named owner will monitor performance, accessibility, data handling, costs, and incidents after launch.
- The review date is on the calendar before the pilot or contract begins.
The defensible university decision is the one that can show what the tool is allowed to do, what evidence supports that use, who remains accountable, and how the institution will stop or change course when the evidence changes.
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